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Publications (10 of 103) Show all publications
Jaradat, S., Dokoohaki, N., Hammar, K., Matskin, M. & Matskin, M. (2019). Dynamic CNN models for fashion recommendation in Instagram. In: Proceedings - 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018: . Paper presented at 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, 11 December 2018 through 13 December 2018 (pp. 1144-1151). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Dynamic CNN models for fashion recommendation in Instagram
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2019 (English)In: Proceedings - 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 1144-1151Conference paper, Published paper (Refereed)
Abstract [en]

Instagram as an online photo-sharing and socialnetworking 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 multilabeled classification compared to the other model.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Dynamic CNN, Dynamic Computation Graph, Fashion Recommendation, Image Classification, Neural Pruning, Text Mining, Classification (of information), Cloud computing, Data mining, Large dataset, Text processing, Dynamic computations, Multiple attributes, Online Photo Sharing, Research challenges, Visual classification, Ubiquitous computing
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-252212 (URN)10.1109/BDCloud.2018.00169 (DOI)000467843200155 ()2-s2.0-85063866491 (Scopus ID)9781728111414 (ISBN)
Conference
16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, 11 December 2018 through 13 December 2018
Note

QC 20190611

Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-08-19Bibliographically approved
Mrazovic, P., Larriba-Pey, J. L. & Matskin, M. (2018). A Deep Learning Approach for Estimating Inventory Rebalancing Demand in Bicycle Sharing Systems. In: Proceedings - International Computer Software and Applications Conference: . Paper presented at 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018, 23 July 2018 through 27 July 2018 (pp. 110-115). IEEE Computer Society
Open this publication in new window or tab >>A Deep Learning Approach for Estimating Inventory Rebalancing Demand in Bicycle Sharing Systems
2018 (English)In: Proceedings - International Computer Software and Applications Conference, IEEE Computer Society , 2018, p. 110-115Conference paper, Published paper (Refereed)
Abstract [en]

Meeting user demand is one of the most challenging problems arising in public bicycle sharing systems. Various factors, such as daily commuting patterns or topographical conditions, can lead to an unbalanced state where the numbers of rented and returned bicycles differ significantly among the stations. This can cause spatial imbalance of the bicycle inventory which becomes critical when stations run completely empty or full, and thus prevent users from renting or returning bicycles. To prevent such service disruptions, we propose to forecast user demand in terms of expected number of bicycle rentals and returns and accordingly to estimate number of bicycles that need to be manually redistributed among the stations by maintenance vehicles. As opposed to traditional solutions to this problem, which rely on short-term demand forecasts, we aim to maximise the time within which the stations remain balanced by forecasting user demand multiple steps ahead of time. We propose a multi-input multi-output deep learning model based on Long Short-Term Memory networks to forecast user demand over long future horizons. Conducted experimental study over real-world dataset confirms the efficiency and accuracy of our approach.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Keywords
Deep learning, Demand prediction, Smart cities, Time series forecasting, Application programs, Bicycles, Forecasting, MIMO systems, Smart city, Sporting goods, Learning approach, Learning models, Multi input multi output, Public bicycle sharing systems, Service disruptions, Short term memory
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-247214 (URN)10.1109/COMPSAC.2018.10213 (DOI)2-s2.0-85055538509 (Scopus ID)9781538626665 (ISBN)
Conference
42nd IEEE Computer Software and Applications Conference, COMPSAC 2018, 23 July 2018 through 27 July 2018
Note

QC 20190415

Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2019-04-15Bibliographically approved
Hammar, K., Jaradat, S., Dokoohaki, N. & Matskin, M. (2018). Deep Text Mining of Instagram Data Without Strong Supervision. In: Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018: . Paper presented at 18th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018; Santiago; Chile; 3 December 2018 through 6 December 2018 (pp. 158-165). IEEE
Open this publication in new window or tab >>Deep Text Mining of Instagram Data Without Strong Supervision
2018 (English)In: Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018, IEEE, 2018, p. 158-165Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Information extraction, Instagram, Weak Supervision, Word Embeddings
National Category
Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:kth:diva-245979 (URN)10.1109/WI.2018.00-94 (DOI)000458968200021 ()2-s2.0-85061892408 (Scopus ID)9781538673256 (ISBN)
Conference
18th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018; Santiago; Chile; 3 December 2018 through 6 December 2018
Note

QC 20190313

Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-03-13Bibliographically approved
Jaradat, S., Dokoohaki, N., Hammar, K., Wara, U. & Matskin, M. (2018). Dynamic CNN Models For Fashion Recommendation in Instagram. In: Chen, JJ Yang, LT (Ed.), 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS: . Paper presented at 16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, DEC 11-13, 2018, Melbourne, AUSTRALIA (pp. 1144-1151). IEEE COMPUTER SOC
Open this publication in new window or tab >>Dynamic CNN Models For Fashion Recommendation in Instagram
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2018 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC, 2018
Series
IEEE International Symposium on Parallel and Distributed Processing with Applications, ISSN 2158-9178
Keywords
Neural Pruning, Dynamic Computation Graph, Dynamic CNN, Image Classification, Text Mining, Fashion Recommendation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-252671 (URN)10.1109/BDCloud.2018.00169 (DOI)000467843200155 ()2-s2.0-85063866491 (Scopus ID)978-1-7281-1141-4 (ISBN)
Conference
16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, DEC 11-13, 2018, Melbourne, AUSTRALIA
Note

QC 20190603

Available from: 2019-06-03 Created: 2019-06-03 Last updated: 2019-06-03Bibliographically approved
Mrazovic, P., Eser, E., Ferhatosmanoglu, H., Larriba-Pey, J. L. & Matskin, M. (2018). Multi-vehicle Route Planning for Efficient Urban Freight Transport. In: JardimGoncalves, R Mendonca, JP Jotsov, V Marques, M Martins, J Bierwolf, R (Ed.), 2018 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS (IS): . Paper presented at The 9th International Conference on Intelligent Systems (IS), 25 - 27 September 2018 Madeira. (pp. 744-753). IEEE
Open this publication in new window or tab >>Multi-vehicle Route Planning for Efficient Urban Freight Transport
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2018 (English)In: 2018 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS (IS) / [ed] JardimGoncalves, R Mendonca, JP Jotsov, V Marques, M Martins, J Bierwolf, R, IEEE , 2018, p. 744-753Conference paper, Published paper (Refereed)
Abstract [en]

The urban parking spaces for loading/unloading are typically over-occupied, which shifts delivery operations to traffic lanes and pavements, increases traffic, generates noise, and causes pollution. We present a data analytics based routing optimization that improves the circulation of vehicles and utilization of parking spaces. We formalize this new problem and develop a novel multi vehicle route planner that avoids congestions at loading/unloading areas and minimizes the total duration. We present the developed tool with an illustration and analysis for the urban freight in the city of Barcelona, which monitors tens of thousands of deliveries every day. Our system includes an effective evaluation of candidate routes by considering the waiting times and further delays of other deliverers as a first class citizen in the optimization. A two-layer local search is proposed with a greedy randomized adaptive method for variable neighborhood search. Our approach is applied and validated over data collected across Barcelona's urban freight transport network, which contains 3,704,034 parking activities. Our solution is shown to significantly improve the use of available parking spaces and the circulation of vehicles, as evidenced by the results. The analysis also provides useful insights on how to manage delivery routes and parking spaces for sustainable urban freight transport and city logistics.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
urban freight transport, route optimization, traveling salesman problem, routing, planning
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-254055 (URN)000469337900109 ()2-s2.0-85065967179 (Scopus ID)
Conference
The 9th International Conference on Intelligent Systems (IS), 25 - 27 September 2018 Madeira.
Note

QC 20190813

Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13Bibliographically approved
Fernando, T., Gureev, N., Matskin, M., Zwick, M. & Natschlager, T. (2018). WorkflowDSL: Scalable Workflow Execution with Provenance for Data Analysis Applications. In: Proceedings - International Computer Software and Applications Conference: . Paper presented at 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018, 23 July 2018 through 27 July 2018 (pp. 774-779). IEEE Computer Society
Open this publication in new window or tab >>WorkflowDSL: Scalable Workflow Execution with Provenance for Data Analysis Applications
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2018 (English)In: Proceedings - International Computer Software and Applications Conference, IEEE Computer Society , 2018, p. 774-779Conference paper, Published paper (Refereed)
Abstract [en]

Data analysis projects typically use different programming languages (from Python for prototyping to C++ for support of runtime constraints) at their different stages by different experts. This creates a need for a data processing framework that is re-usable across multiple programming languages and supports collaboration of experts. In this work, we discuss implementation of a framework which uses a Domain Specific Language (DSL), called WorkflowDSL, that enables domain experts to collaborate on fine-tuning workflows. The framework includes support for parallel execution without any specialized code. It also provides a provenance capturing framework that enables users to analyse past executions and retrieve complete lineage of any data item generated. Graph database is used for storing provenance data. Advantages of usage of a graph database compare to relational databases are demonstrated. Experiments which were performed using a real-world scientific workflow from the bioinformatics domain and industrial data analysis models show that users were able to execute workflows efficiently when using WorkflowDSL for workflow composition and Python for task implementations. Moreover, we show that capturing provenance data can be useful for analysing past workflow executions.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Keywords
Data analysis workflows, Linage, Parallel execution, Provenance, Application programs, C++ (programming language), Graph Databases, Information analysis, Problem oriented languages, Software prototyping, Domain specific language (DSL), Parallel executions, Relational Database, Scientific workflows, Work-flows, Workflow composition, Data handling
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-247215 (URN)10.1109/COMPSAC.2018.00115 (DOI)2-s2.0-85055419742 (Scopus ID)9781538626665 (ISBN)
Conference
42nd IEEE Computer Software and Applications Conference, COMPSAC 2018, 23 July 2018 through 27 July 2018
Note

QC 20190415

Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2019-04-15Bibliographically approved
Mrazovic, P., De La Rubia, I., Urmeneta, J., Balufo, C., Tapias, R., Matskin, M. & Larriba-Pey, J. L. (2016). CIGO! Mobility Management Platform for Growing Efficient and Balanced Smart City Ecosystem. In: IEEE SECOND INTERNATIONAL SMART CITIES CONFERENCE (ISC2 2016): . Paper presented at 2nd IEEE International Smart Cities Conference (ISC2), SEP 12-15, 2016, Trento, ITALY (pp. 106-109). IEEE
Open this publication in new window or tab >>CIGO! Mobility Management Platform for Growing Efficient and Balanced Smart City Ecosystem
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2016 (English)In: IEEE SECOND INTERNATIONAL SMART CITIES CONFERENCE (ISC2 2016), IEEE, 2016, p. 106-109Conference paper, Published paper (Refereed)
Abstract [en]

The massive amount of tourists, citizens and traffic in big cities usually collapse busy areas causing transport inefficiency, unbalanced economic growth, crime, and nuisance among citizens and visitors. Therefore, the Smart City strategies such as Smart Mobility and Smart Governance naturally arise as means to improve mobility in urban areas. In this paper we propose a novel mobility management platform and business model that can attract numerous actors and still be orchestrated by the city government. The proposed platform integrates mobility data from various sources such as Open Data, mobile applications, sensors and government data, allowing for its visualisation and analysis while making it actionable through associated third party mobile applications. We propose to inject the city mobility policies to the third party mobile applications which provide services related to the city resources. In this way we form a value chain which connects different actors (city governments, mobile application providers, POI owners, companies that require logistics in cities, and final users) who both take a part in improving the mobility in urban areas, and benefit from the way mobility policies being executed. In this paper we discuss the business model and logical architecture of the proposed platform which has been already deployed in the city of Barcelona.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
smart cities, smart mobility, mobility policies
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-202502 (URN)10.1109/ISC2.2016.7580750 (DOI)000392263700020 ()2-s2.0-84994127946 (Scopus ID)978-1-5090-1845-1 (ISBN)
Conference
2nd IEEE International Smart Cities Conference (ISC2), SEP 12-15, 2016, Trento, ITALY
Note

QC 20170228

Available from: 2017-02-28 Created: 2017-02-28 Last updated: 2017-03-07Bibliographically approved
Sato, H., Matskin, M. & Claycomb, W. (2016). Message from the Program Chairs-in-Chief. Paper presented at 10 June 2016 through 14 June 2016. Computer Software and Applications Conference, 1, Article ID 7551982.
Open this publication in new window or tab >>Message from the Program Chairs-in-Chief
2016 (English)In: Computer Software and Applications Conference, ISSN 0730-3157, Vol. 1, article id 7551982Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE Computer Society, 2016
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-194940 (URN)10.1109/COMPSAC.2016.14 (DOI)2-s2.0-84988039968 (Scopus ID)
Conference
10 June 2016 through 14 June 2016
Note

QC 20161124

Available from: 2016-11-24 Created: 2016-11-01 Last updated: 2017-11-29Bibliographically approved
Sato, H., Matskin, M. & Claycomb, W. (2016). Message from the Program Chairs-in-Chief - Volume 2. Paper presented at 10 June 2016 through 14 June 2016. Computer Software and Applications Conference, 2, Article ID 7551963.
Open this publication in new window or tab >>Message from the Program Chairs-in-Chief - Volume 2
2016 (English)In: Computer Software and Applications Conference, ISSN 0730-3157, Vol. 2, article id 7551963Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE Computer Society, 2016
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-194941 (URN)10.1109/COMPSAC.2016.241 (DOI)2-s2.0-84988037177 (Scopus ID)
Conference
10 June 2016 through 14 June 2016
Note

QC 20161124

Available from: 2016-11-24 Created: 2016-11-01 Last updated: 2017-11-29Bibliographically approved
Jaradat, S., Dokoohaki, N., Matskin, M. & Ferrari, E. (2016). Trust And Privacy Correlations in Social Networks: A Deep Learning Framework. In: PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016: . Paper presented at 8th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), AUG 18-21, 2016, San Francisco, CA (pp. 203-206). IEEE conference proceedings
Open this publication in new window or tab >>Trust And Privacy Correlations in Social Networks: A Deep Learning Framework
2016 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-200264 (URN)000390760100031 ()2-s2.0-85006765626 (Scopus ID)978-1-5090-2846-7 (ISBN)
Conference
8th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), AUG 18-21, 2016, San Francisco, CA
Note

QC 20170130

Available from: 2017-01-30 Created: 2017-01-23 Last updated: 2018-01-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4722-0823

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