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  • 1.
    Bunea, Ramona
    et al.
    KTH, School of Information and Communication Technology (ICT).
    Mokarizadeh, Shahab
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Exploiting dynamic privacy in socially regularized recommenders2012In: Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on, IEEE , 2012, p. 539-546Conference paper (Refereed)
    Abstract [en]

    In this paper we introduce a privacy-aware collaborative filtering recommender framework which aims to address the privacy concern of profile owners in the context of social trust sparsity. While sparsity in social trust is mitigated by similarity driven trust using a probabilistic matrix factorization technique, the privacy issue is addressed by employing a dynamic privacy inference model. The privacy inference model exploits the underlying inter-entity trust information to obtain a personalized privacy view for each individual in the social network. We evaluate the proposed framework by employing an off-the-shelf collaborative filtering recommender method to make predictions using this personalized view. Experimental results show that our method offers better performance than similar non-privacy aware approaches, while at the same time meeting user privacy concerns.

  • 2. Cena, Federica
    et al.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Forging Trust and Privacy with User Modeling Frameworks: An Ontological Analysis2011In: The First International Conference on Social Eco-Informatics: (SOTICS 2011) / [ed] Dokoohaki and Hall, IARIA , 2011, p. 43-48Conference paper (Refereed)
    Abstract [en]

    With the ever increasing importance of social net- working sites and services, socially intelligent agents who are responsible for gathering, managing and maintaining knowledge surrounding individual users are of increasing interest to both computing research communities as well as industries. For these agents to be able to fully capture and manage the knowledge about a user’s interaction with these social sites and services, a social user model needs to be introduced. A social user model is defined as a generic user model (model capable of capturing generic information related to a user), plus social dimensions of users (models capturing social aspects of user such as activities and social contexts). While existing models capture a proportion of such information, they fail to model and present ones of the most important dimensions of social connectivity: trust and privacy. To this end, in this paper, we introduce an ontological model of social user, composed by a generic user model component, which imports existing well-known user model structures, a social model, which contains social dimensions, and trust, reputation and privacy become the pivotal concepts gluing the whole ontological knowledge models together.

  • 3.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Deliverable D2.1 - Report of User Profile Formal Represen-tation and Metadata Keyword Extension: EU FP7 Smartmuseum project Scientific Deliverable2008Report (Other academic)
    Abstract [en]

    SMARTMUSEUM (Cultural Heritage Knowledge Exchange Platform) is a Research and Development project sponsored under theEuropeans Commission’s 7th Framework. The overall objective of the project is to develop a platform for innovative servicesenhancing on-site personalized access to digital cultural heritage through adaptive and privacy preserving user profiling. Using on-site knowledge databases, global digital libraries and visitors’ experiential knowledge, the platform makes possible the creation ofinnovative multilingual services for increasing interaction between visitors and cultural heritage objects in a future smart museumenvironment, taking full benefit of digitized cultural information.The main objective of this deliverable is to deliver formalization for user profile format as well as giving an extension of keywordsused to describe the human side of access to cultural heritage.

  • 4.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Trust-Based User Profiling2013Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    We have introduced the notion of user profiling with trust, as a solution to theproblem of uncertainty and unmanageable exposure of personal data duringaccess, retrieval and consumption by web applications. Our solution sug-gests explicit modeling of trust and embedding trust metrics and mechanismswithin very fabric of user profiles. This has in turn allowed information sys-tems to consume and understand this extra knowledge in order to improveinteraction and collaboration among individuals and system. When formaliz-ing such profiles, another challenge is to realize increasingly important notionof privacy preferences of users. Thus, the profiles are designed in a way toincorporate preferences of users allowing target systems to understand pri-vacy concerns of users during their interaction. A majority of contributionsof this work had impact on profiling and recommendation in digital librariescontext, and was implemented in the framework of EU FP7 Smartmuseumproject. Highlighted results start from modeling of adaptive user profilesincorporating users taste, trust and privacy preferences. This in turn led toproposal of several ontologies for user and content characteristics modeling forimproving indexing and retrieval of user content and profiles across the plat-form. Sparsity and uncertainty of profiles were studied through frameworksof data mining and machine learning of profile data taken from on-line so-cial networks. Results of mining and population of data from social networksalong with profile data increased the accuracy of intelligent suggestions madeby system to improving navigation of users in on-line and off-line museum in-terfaces. We also introduced several trust-based recommendation techniquesand frameworks capable of mining implicit and explicit trust across ratingsnetworks taken from social and opinion web. Resulting recommendation al-gorithms have shown to increase accuracy of profiles, through incorporationof knowledge of items and users and diffusing them along the trust networks.At the same time focusing on automated distributed management of profiles,we showed that coverage of system can be increased effectively, surpassingcomparable state of art techniques. We have clearly shown that trust clearlyelevates accuracy of suggestions predicted by system. To assure overall pri-vacy of such value-laden systems, privacy was given a direct focus when archi-tectures and metrics were proposed and shown that a joint optimal setting foraccuracy and perturbation techniques can maintain accurate output. Finally,focusing on hybrid models of web data and recommendations motivated usto study impact of trust in the context of topic-driven recommendation insocial and opinion media, which in turn helped us to show that leveragingcontent-driven and tie-strength networks can improve systems accuracy forseveral important web computing tasks.

  • 5.
    Dokoohaki, Nima
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Kaleli, Cihan
    Polat, Huseyin
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Achieving Optimal Privacy in Trust-Aware Social Recommender Systems2010In: SOCIAL INFORMATICS / [ed] Bolc L; Makowski M; Wierzbicki A, 2010, Vol. 6430, p. 62-79Conference paper (Refereed)
    Abstract [en]

    Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers proposed for utilization of interpersonal trust between users, to alleviate many of these crucial shortcomings. Till now, attention has been mainly paid to strong points about trust-aware recommenders such as alleviating profile sparsity or calculation cost efficiency, while least attention has been paid on investigating the notion of privacy surrounding the disclosure of individual ratings and most importantly protection of trust computation across social networks forming the backbone of these systems. To contribute to addressing problem of privacy in trust-aware recommenders, within this paper, first we introduce a framework for enabling privacy-preserving trust-aware recommendation generation. While trust mechanism aims at elevating recommenders accuracy, to preserve privacy, accuracy of the system needs to be decreased. Since within this context, privacy and accuracy are conflicting goals we show that a Pareto set can be found as an optimal setting for both privacy-preserving and trust-enabling mechanisms. We show that this Pareto set, when used as the configuration for measuring the accuracy of base collaborative filtering engine, yields an optimized tradeoff between conflicting goals of privacy and accuracy. We prove this concept along with applicability of our framework by experimenting with accuracy and privacy factors, and we show through experiment how such optimal set can be inferred.

  • 6.
    Dokoohaki, Nima
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    An Adaptive Framework for Discovery andMining of User Profiles from Social Web-based Interest Communities2013In: The Influence of Technology on Social Network Analysis and Mining / [ed] Özyer, T.; Rokne, J.; Wagner, G.; Reuser, A., Wien: Springer, 2013, Vol. 23, p. 497-519Chapter in book (Refereed)
    Abstract [en]

    Abstract Within this paper we introduce an adaptive framework for semi- tofully-automatic discovery, acquisition and mining of topic style interest profilesfrom openly accessible social web communities. To do such, we build an adaptivetaxonomy search tree from target domain (domain towards which we are gatheringand processing profiles for), starting with generic concepts at root moving down tospecific-level instances at leaves, then we utilize one of proposed Quest schemesto read the concept labels from the tree and crawl the source social networkrepositories for profiles containing matching and related topics. Using machinelearning techniques, cached profiles are then mined in two consecutive steps,utilizing a clusterer and a classifier in order to assign and predict correct profilesto their corresponding clustered corpus, which are retrieved later on by an ontology-based recommender to suggest and recommend the community members with theitems of their similar interest. Focusing on increasingly important digital culturalheritage context, using a set of profiles acquired from an openly accessible socialnetwork, we test the accuracy and adaptivity of framework. We will show that a tradeoff between schemes proposed can lead to adaptive discovery of highly relevant profiles.

  • 7.
    Dokoohaki, Nima
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Effective Design of Trust Ontologies for Improvement in the Structure of Socio-Semantic Trust Networks2008In: International Journal On Advances in Intelligent Systems, ISSN 1942-2679, Vol. 1, no 1, p. 23-42Article in journal (Refereed)
  • 8.
    Dokoohaki, Nima
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Mining divergent opinion trust networks through latent dirichlet allocation2012In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE , 2012, p. 879-886Conference paper (Refereed)
    Abstract [en]

    While the focus of trust research has been mainly on defining and modeling various notions of social trust, less attention has been given to modeling opinion trust. When speaking of social trust mainly homophily (similarity) has been the most successful metric for learning trustworthy links, specially in social web applications such as collaborative filtering recommendation systems. While pure homophily such as Pearson coefficient correlation and its variations, have been favorable to finding taste distances between individuals based on their rated items, they are not necessarily useful in finding opinion distances between individuals discussing a trending topic, e. g. Arab spring. At the same time text mining techniques, such as vector-based techniques, are not capable of capturing important factors such as saliency or polarity which are possible with topical models for detecting, analyzing and suggesting aspects of people mentioning those tags or topics. Thus, in this paper we are proposing to model opinion distances using probabilistic information divergence as a metric for measuring the distances between people's opinion contributing to a discussion in a social network. To acquire feature sets from topics discussed in a discussion we use a very successful topic modeling technique, namely Latent Dirichlet Allocation (LDA). We use the distributions resulting to model topics for generating social networks of group and individual users. Using a Twitter dataset we show that learned graphs exhibit properties of real-world like networks.

  • 9.
    Dokoohaki, Nima
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Personalizing Human Interaction through Hybrid Ontological Profiling: Cultural Heritage Case Study2008In: 1st Workshop on Semantic Web Applications and Human Aspects, (SWAHA08), 2008, p. 133-140Conference paper (Refereed)
    Abstract [en]

    In this paper we present a novel user profile formalization, which allows describingthe user attributes as well as history of user access for personalized, adaptive and interactiveexperience while we believe that our approach is applicable to different semantic applicationswe illustrate our solution in the context of online and onsite museums and exhibits visit. Weargue that a generic structure will allow incorporation of multiple dimensions of user attributesand characteristics as well as allowing different abstraction levels for profile formalization andpresentations. In order to construct such profile structures we extend and enrich existingmetadata vocabularies for cultural heritage to contain keywords pertaining to usage attributesand user related keywords. By extending metadata vocabularies we allow improvedmatchmaking between extended user profile contents and cultural heritage contents. Thisextension creates the possibility of further personalization of access to cultural heritageavailable through online and onsite digital libraries.

  • 10.
    Dokoohaki, Nima
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
    Matskin, Mihhail
    Norwegian University of Science and Technology.
    Quest: An Adaptive Framework for User Profile Acquisition from Social Communities of Interest2010In: Proceedings - 2010 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2010, 2010, p. 360-364Conference paper (Refereed)
    Abstract [en]

    Within this paper we introduce a framework for semi- to full-automatic discovery and acquisition of bag-of-words style interest profiles from openly accessible Social Web communities. To do such, we construct a semantic taxonomy search tree from target domain (domain towards which we're acquiring profiles for), starting with generic concepts at root down to specific-level instances at leaves, then we utilize one of proposed Quest methods, namely Depth-based, N-Split and Greedy to read the concept labels from the tree and crawl the source Social Network for profiles containing corresponding topics. Cached profiles are then mined in a two-step approach, using a clusterer and a classifier to generate predictive model presenting weighted profiles, which are used later on by a semantic recommender to suggest and recommend the community members with the items of their similar interest.

  • 11.
    Dokoohaki, Nima
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Reasoning about Weighted Semantic User Profiles through Collective Confidence Analysis: A Fuzzy Evaluation2010In: ADVANCES IN INTELLIGENT WEB MASTERING-2, PROCEEDINGS    / [ed] Snasel V; Szczepaniak PS; Abraham A; Kacprzyk J, 2010, Vol. 67, p. 71-81Conference paper (Refereed)
    Abstract [en]

    User profiles are vastly utilized to alleviate the increasing problem of so called information overload. Many important issues of Semantic Web like trust, privacy, matching and ranking have a certain degree of vagueness and involve truth degrees that one requires to present and reason about. In this ground, profiles tend to be useful and allow incorporation of these uncertain attributes in the form of weights into profiled materials. In order to interpret and reason about these uncertain values, we have constructed a fuzzy confidence model, through which these values could be collectively analyzed and interpreted as collective experience confidence of users. We analyze this model within a scenario, comprising weighted user profiles of a semantically enabled cultural heritage knowledge platform. Initial simulation results have shown the benefits of our mechanism for alleviating problem of sparse and empty profiles.

  • 12.
    Dokoohaki, Nima
    et al.
    KTH, School of Information and Communication Technology (ICT), Microelectronics and Information Technology, IMIT.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Microelectronics and Information Technology, IMIT.
    Structural Determination of Ontology-Driven Trust Networks in Semantic Social Institutions and Ecosystems2007In: Proceedings of the International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM'07) and the International Conference on Advances in Semantic Processing SEMAPRO, 2007, p. 263-268Conference paper (Refereed)
    Abstract [en]

    Social institutions and ecosystems are growing across the web and social trust networks formed within these systems create an extraordinary test-bed to study relation dependant notions such as trust, reputation and belief. In order to capture, model and represent the semantics of trust relationships forming the trust networks, main components of relationships are represented and described using ontologies. This paper investigates how effective design of trust ontologies can improve the structure of trust networks created and implemented within semantic web-driven social institutions and systems. Based on the context of our research, we represent a trust ontology that captures the semantics of the structure of trust networks based on the context of social institutions and ecosystems on semantic web.

  • 13.
    Dokoohaki, Nima
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Afzal, Usman
    KTH, School of Information and Communication Technology (ICT).
    Islam, Md. Mistamikul
    KTH, School of Information and Communication Technology (ICT).
    An Enterprise Social Recommendation System for Connecting Swedish Professionals2014In: Proceedings - IEEE 38th Annual International Computers, Software and Applications Conference Workshops, COMPSACW 2014 / [ed] Carl K. Chang, Yan Gao, Ali Hurson, Mihhail Matskin, Bruce McMillin, Yasuo Okabe, IEEE Communications Society, 2014, p. 234-239Conference paper (Refereed)
    Abstract [en]

    Most cooperative businesses rely on some form of social networking system to facilitate user profiling and networking of their employees. To facilitate the discovery, matchmaking and networking among the co-workers across the enterprises social recommendation systems are often used. Off-the-shelf nature of these components often makes it hard for individuals to control their exposure as well as their preferences of whom to connect to. To this end, trust based recommenders have been amongst the most popular and demanding solutions due to their advantage of using social trust to generate more accurate suggestions for peers to connect to. They also allow individuals to control their exposure based on explicit trust levels. In this work we have proposed for an enterprise trust-based recommendation system with privacy controls. To generate accurate predictions, a local trust metric is defined between users based on correlations of user's profiled content such as blogging, articles wrote, comments, and likes along with profile information such as organization, region, interests or skills. Privacy metric is defined in such a way that users have full freedom either to hide their data from the recommender or customize their profiles to make them visible only to users with defined level of trustworthy.

  • 14.
    Dokoohaki, Nima
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Ruotsalo, Tuukka
    Helsinki Institute for Information Technology.
    Kauppinen, Tomi
    Helsinki Institute for Information Technology.
    Mäkelä, Eetu
    Helsinki Institute for Information Technology.
    Deliverable 2.2 -Report describing methods for dynamic user profile creation: EU FP7 Smartmuseum Scientific Deliverable2009Report (Other (popular science, discussion, etc.))
    Abstract [en]

    SMARTMUSEUM (Cultural Heritage Knowledge Exchange Platform) is a Research and Development project sponsored under theEuropeans Commission’s 7th Framework. The overall objective of the project is to develop a platform for innovative servicesenhancing on-site personalized access to digital cultural heritage through adaptive and privacy preserving user profiling. Using on-site knowledge databases, global digital libraries and visitors’ experiential knowledge, the platform makes possible the creation ofinnovative multilingual services for increasing interaction between visitors and cultural heritage objects in a future smart museumenvironment, taking full benefit of digitized cultural information.The main objective of this deliverable is to describe a theoretical framework for management of dynamic user profiles.

  • 15.
    Fazeli, Soude
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Zarghami, Alireza
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
    Elevating Prediction Accuracy in Trust-aware Collaborative Filtering Recommenders through T-index Metric and TopTrustee lists2010In: Journal of Emerging Technologies in Web Intelligence, ISSN 1798-0461, Vol. 2, no 4, p. 300-309Article in journal (Refereed)
    Abstract [en]

    The growing popularity of Social Networks raises the important issue of trust. Among many systems which have realized the impact of trust, Recommender Systems have been the most influential ones. Collaborative Filtering Recommenders take advantage of trust relations between users for generating more accurate predictions. In this paper, we propose a semantic recommendation framework for creating trust relationships among all types of users with respect to different types of items, which are accessed by unique URI across heterogeneous networks and environments. We gradually build up the trust relationships between users based on the rating information from user profiles and item profiles to generate trust networks of users. For analyzing the formation of trust networks, we employ Tindex as an estimate of a user’s trustworthiness to identify and select neighbors in an effective manner. In this work, we utilize T-index to form the list of an item’s raters, called TopTrustee list for keeping the most reliable users who have already shown interest in the respective item. Thus, when a user rates an item, he/she is able to find users who can be trustworthy neighbors even though they might not be accessible within an upper bound of traversal path length. An empirical evaluation demonstrates how T-index improves the Trust Network structure by generating connections to more trustworthy users. We also show that exploiting Tindex results in better prediction accuracy and coverage of recommendations collected along few edges that connect users on a Social Network.

  • 16.
    Fazeli, Soude
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Zarghami, Alireza
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Mechanizing Social Trust-Aware Recommenders with T-Index Augmented Trustworthiness2010In: TRUST, PRIVACY AND SECURITY IN DIGITAL BUSINESS  / [ed] Katsikas S; Lopez J; Soriano M, 2010, Vol. 6264, p. 202-213Conference paper (Refereed)
    Abstract [en]

    Social Networks have dominated growth and popularity of the Web to an extent which has never been witnessed before. Such popularity puts forward issue of trust to the participants of Social Networks. Collaborative Filtering Recommenders have been among many systems which have begun taking full advantage of Social Trust phenomena for generating more accurate predictions. For analyzing the evolution of constructed networks of trust, we utilize Collaborative Filtering enhanced with T-index as an estimate of a user's trustworthiness to identify and select neighbors in an effective manner. Our empirical evaluation demonstrates how T-index improves the Trust Network structure by generating connections to more trustworthy users. We also show that exploiting T-index results in better prediction accuracy and coverage of recommendations collected along few edges that connect users on a network.

  • 17. Guo, M.
    et al.
    Yang, K.
    Musial-Gabrys, K.
    Min, G.
    Yin, H.
    Nguyen, N. P.
    Jiang, Y.
    Kourtellis, N.
    Cheng, X.
    Leng, S.
    Wang, H.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
    Message from the MSNCom 2015 workshop chairs2015In: Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015, Institute of Electrical and Electronics Engineers (IEEE), 2015, article id 7362990Conference paper (Refereed)
  • 18.
    Hammar, Kim
    et al.
    KTH.
    Jaradat, Shatha
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Dokoohaki, Nima
    KTH.
    Matskin, Mihhail
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Deep Text Mining of Instagram Data Without Strong Supervision2018In: Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018, IEEE, 2018, p. 158-165Conference paper (Refereed)
    Abstract [en]

    With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. This textual data can be analyzed for the purpose of improving user recommendations and detecting trends. Instagram is one of the largest social media platforms, containing both text and images. However, most of the prior research on text processing in social media is focused on analyzing Twitter data, and little attention has been paid to text mining of Instagram data. Moreover, many text mining methods rely on annotated training data, which in practice is both difficult and expensive to obtain. In this paper, we present methods for unsupervised mining of fashion attributes from Instagram text, which can enable a new kind of user recommendation in the fashion domain. In this context, we analyze a corpora of Instagram posts from the fashion domain, introduce a system for extracting fashion attributes from Instagram, and train a deep clothing classifier with weak supervision to classify Instagram posts based on the associated text. With our experiments, we confirm that word embeddings are a useful asset for information extraction. Experimental results show that information extraction using word embeddings outperforms a baseline that uses Levenshtein distance. The results also show the benefit of combining weak supervision signals using generative models instead of majority voting. Using weak supervision and generative modeling, an F1 score of 0.61 is achieved on the task of classifying the image contents of Instagram posts based solely on the associated text, which is on level with human performance. Finally, our empirical study provides one of the few available studies on Instagram text and shows that the text is noisy, that the text distribution exhibits the long-tail phenomenon, and that comment sections on Instagram are multi-lingual.

  • 19.
    Jaradat, Shatha
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Dokoohaki, Nima
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Hammar, Kim
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Wara, Ummal
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Dynamic CNN Models For Fashion Recommendation in Instagram2018In: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, p. 1144-1151Conference paper (Refereed)
    Abstract [en]

    Instagram as an online photo-sharing and social-networking service is becoming more powerful in enabling fashion brands to ramp up their business growth. Nowadays, a single post by a fashion influencer attracts a wealth of attention and a magnitude of followers who are curious to know more about the brands and style of each clothing item sitting inside the image. To this end, the development of efficient Deep CNN models that can accurately detect styles and brands have become a research challenge. In addition, current techniques need to cope with inherent fashion-related data issues. Namely, clothing details inside a single image only cover a small proportion of the large and hierarchical space of possible brands and clothing item attributes. In order to cope with these challenges, one can argue that neural classifiers should become adapted to large-scale and hierarchical fashion datasets. As a remedy, we propose two novel techniques to incorporate the valuable social media textual content to support the visual classification in a dynamic way. The first method is adaptive neural pruning (DynamicPruning) in which the clothing item category detected from posts' text analysis can be used to activate the possible range of connections of clothing attributes' classifier. The second method (DynamicLayers) is a dynamic framework in which multiple-attributes classification layers exist and a suitable attributes' classifier layer is activated dynamically based upon the mined text from the image. Extensive experiments on a dataset gathered from Instagram and a baseline fashion dataset (DeepFashion) have demonstrated that our approaches can improve the accuracy by about 20% when compared to base architectures. It is worth highlighting that with Dynamiclayers we have gained 35% accuracy for the task of multi-class multi-labeled classification compared to the other model.

  • 20.
    Jaradat, Shatha
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Ferrari, Elena
    Trust And Privacy Correlations in Social Networks: A Deep Learning Framework2016In: PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, IEEE conference proceedings, 2016, p. 203-206Conference paper (Refereed)
    Abstract [en]

    Online Social Networks (OSNs) remain the focal point of Internet usage. Since the beginning, networking sites tried best to have right privacy mechanisms in place for users, enabling them to share the right content with the right audience. With all these efforts, privacy customizations remain hard for users across the sites. Existing research that address this problem mainly focus on semi-supervised strategies that introduce extra complexity by requiring the user to manually specify initial privacy preferences for their friends. In this work, we suggest an adaptive solution that can dynamically generate privacy labels for users in OSNs. To this end, we introduce a deep reinforcement learning framework that targets two key problems in OSNs like Facebook: the exposure of users' interactions through the network to less trusted direct friends, and the possibility of propagating user updates through direct friends' interactions to indirect friends. By implementing this framework, we aim at understanding how social trust and privacy could be correlated, specifically in a dynamic fashion. We report the ranked dependence between the generated privacy labels and the estimated user trust values, which indicate the ability of the framework to identify the highly trusted users and share with them higher percentages of data.

  • 21. Krestel, Ralf
    et al.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Diversifying Product Review Rankings: Getting the Full Picture2011In: 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Washington DC: IEEE Computer Society Digital Library, 2011, p. 138-145Conference paper (Refereed)
    Abstract [en]

    E-commerce Web sites owe much of their popularityto consumer reviews provided together with product descriptions.On-line customers spend hours and hours going through heaps oftextual reviews to build confidence in products they are planningto buy. At the same time, popular products have thousands ofuser-generated reviews. Current approaches to present them tothe user or recommend an individual review for a product arebased on the helpfulness or usefulness of each review. In thispaper we look at the top-k reviews in a ranking to give a goodsummary to the user with each review complementing the others.To this end we use Latent Dirichlet Allocation to detect latenttopics within reviews and make use of the assigned star ratingfor the product as an indicator of the polarity expressed towardsthe product and the latent topics within the review. We present aframework to cover different ranking strategies based on theuser’s need: Summarizing all reviews; focus on a particularlatent topic; or focus on positive, negative or neutral aspects.We evaluated the system using manually annotated review datafrom a commercial review Web site.

  • 22.
    Krestel, Ralf
    et al.
    University of California, Irvine, CA, USA.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Ranking Product ReviewsArticle in journal (Other academic)
    Abstract [en]

    E-commerce Web sites owe much of their popularity to consumer reviews provided together with productdescriptions. On-line customers spend hours and hours going through heaps of textual reviews to buildconfidence in products they are planning to buy. At the same time, popular products have thousands of user-generated reviews. Current approaches to present them to the user or recommend an individual review for aproduct are based on the helpfulness or usefulness of each review. In this paper we look at the top-k reviewsin a ranking to give a good summary to the user with each review complementing the others. To this endwe use Latent Dirichlet Allocation to detect latent topics within reviews and make use of the assigned starrating for the product as an indicator of the polarity expressed towards the product and the latent topicswithin the review. We present a framework to cover different ranking strategies based on the user’s need:Summarizing all reviews; focus on a particular latent topic; or focus on positive, negative or neutral aspects.We evaluated the system using manually annotated review data from a commercial review Web site.

  • 23.
    Magureanu, Stefan
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Mokarizadeh, Shahab
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Design and Analysis of a Gossip-based Decentralized Trust Recommender System2012In: 4th ACM Recommender Systems (RecSys) Workshop on Recommender Systems & the Social Web, 2012Conference paper (Refereed)
    Abstract [en]

    Information overload has become an increasingly common problem in today’s large scale internet applications. Collaborative filtering(CF) recommendation systems have emerged   as a popular solution to this problem by taking advantage of underlying social networks. Traditional CF recommenders suffer from lack of scalability[18] while decentralized recommendation systems (DHT-based, Gossip-based etc.) have promised to alleviate this problem. Thus, in this paper we propose a decentralized approach to CF recommender sys  tems that takes advantage of the popular P2P T-Man algorithm to create and maintain an overlay network capable of generating predictions based on only local information. We       analyze our approaches performance in terms of prediction accuracy and item-coverage function of neighborhood size as well as number of T-Man rounds. We show our system       achieves better accuracy than previous approaches while implementing a highly scalable, decentralized paradigm. We also show our system is able to generate predictions for a       large fraction of users, which is comparable with the centralized approaches.

  • 24.
    Magureanu, Stefan
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Mokarizadeh, Shahab
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Epidemic trust-based recommender systems2012In: Proceedings - 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust and 2012 ASE/IEEE International Conference on Social Computing, SocialCom/PASSAT 2012, IEEE , 2012, p. 461-470Conference paper (Refereed)
    Abstract [en]

    Collaborative filtering(CF) recommender systems are among the most popular approaches to solving the information overload problem in social networks by generating accurate predictions based on the ratings of similar users. Traditional CF recommenders suffer from lack of scalability while decentralized CF recommenders (DHT-based, Gossip-based etc.) have promised to alleviate this problem. Thus, in this paper we propose a decentralized approach to CF recommender systems that uses the T-Man algorithm to create and maintain an overlay network that in turn would facilitate the generation of recommendations based on local information of a node. We analyse the influence of the number of rounds and neighbors on the accuracy of prediction and item coverage and we propose a new approach to inferring trust values between a user and its neighbors. Our experiment son two datasets show an improvement of prediction accuracy relative to previous approaches while using a highly scalable, decentralized paradigm. We also analyse item coverage and show that our system is able to generate predictions for significant fraction of the users, which is comparable with the centralized approaches.

  • 25.
    Mokarizadeh, Shahab
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Bunea, Ramona
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Enhancing Social Matrix Factorization with Privacy2013In: Proceedings of the ACM Symposium on Applied Computing, 2013, p. 277-278Conference paper (Refereed)
    Abstract [en]

    Within the course of this manuscript we present a privacy-preserving collaborative filtering recommender system whichaims at alleviating the concern with privacy of user pro-files within the context of sparse social trust data. Whileproblem of sparsity in social trust is often addressed by tak-ing similarity driven trust measures through a probabilisticmatrix factorization technique, we address the issue of pri-vacy by proposing a dynamic privacy inference model. Theprivacy inference model exploits the underlying inter-entitytrust information in order to build a personalized privacyperspective for each individual within the social network.This is followed by our evaluation of the proposed solutionby adopting an off-the-shelf collaborative filtering recom-mender library, in order to generate predictions using thispersonalized view.

  • 26.
    Mokarizadeh, Shahab
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Küngas, Peep
    Trust and Privacy Enabled Service Composition using Social Experience2010In: 10th IFIP International Conference on e-business,e-services and e-society (I3E), Springer, 2010, p. 226-236Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a framework for automatic selection andcomposition of services which exploits trustworthiness of services as a metric formeasuring the quality of service composition. Trustworthiness is defined in terms ofservice reputation extracted from user profiles. The profiles are, in particular, extractedand inferred from a social network which accumulates users past experience withcorresponding services. Using our privacy inference model we, first, prune socialnetwork to hide privacy sensitive contents and, then, utilize a trust inference basedalgorithm to measure reputation score of each individual service, and subsequentlytrustworthiness of their composition

  • 27. Ruotsalo, Tuukka
    et al.
    Makela, Eetu
    Kauppinen, Tomi
    Hyvonen, Eero
    Haav, Krister
    Rantala, Ville
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS.
    SmartMuseum: Personalized Context-aware Access to Digital Cultural Heritage2009In: Proceedings of International Conference for Digital Libraries and the Semantic Web (ICSD 2009), 2009Conference paper (Refereed)
  • 28. Sandberg, Linn
    et al.
    Jaradat, Shatha
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    The social media election agenda Issue salience on Twitter during the European and Swedish 2014 elections2016In: PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, IEEE conference proceedings, 2016, p. 793-794Conference paper (Refereed)
    Abstract [en]

    the role of issues in electoral preference formation has long been an established key factor and what voters consider the most important problem i.e. issue salience is essential for party choice. Political issues (and their salience to the electorate) also play an important role in parties' tactical campaign strategies. This study examines to what extent social media possibly can contribute in shaping the issue agenda regarding the political parties. The issue agenda on Twitter is likely to have its own characteristics and dynamics, shaped by the technical peculiarities, users and the new campaigning possibilities that social media offers. This study will identify what issues are salient in the online discussions in conjunction with the European election and Swedish national election 2014. The distribution of issue attention divided to the various parties on social media is analyzed in light of the issue agenda set forth by the voters for the different elections.

  • 29.
    Zarghami, Alireza
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Fazeli, Soude
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Dokoohaki, Nima
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
    Matskin, Mihhail
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
    Social Trust-aware Recommendation System: A T-Index Approach2009In: 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 3 / [ed] BaezaYates R; Berendt B; Bertino E; Lim EP; Pasi G, LOS ALAMITOS: IEEE COMPUTER SOC , 2009, p. 85-90Conference paper (Refereed)
    Abstract [en]

    Collaborative Filtering based on similarity suffers from a variety of problems such as sparsity and scalability In this paper, we propose an ontological model of trust between users on a social network to address the limitations of similarity measure in Collaborative Filtering algorithms. For enhancing the constructed network of users based on trust, we introduce an estimate of a user's trustworthiness called T-index to identify and select neighbors in an effective manner We employ T-index to store raters of an item in a so-called TopTrustee list which provides information about users who might not be accessible within a predefined maximum path length. An empirical evaluation shows that our solution improves both prediction accuracy and coverage of recommendations collected along few edges that connect users on a social network by exploiting T-index. We also analyze effect of T-index on structure of trust network to justify the results.

1 - 29 of 29
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