Change search
ReferencesLink to record
Permanent link

Direct link
Ranking Product Reviews
University of California, Irvine, CA, USA.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
(English)Article in journal (Other academic) Submitted
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.

Keyword [en]
Ranking, Topic Models, Summarization, Diversification, Review Recommendation
National Category
Information Systems
URN: urn:nbn:se:kth:diva-118474OAI: diva2:606429
2011 IEEE/WIC/ACM International Conference on Web Intelligence (WI’11)

QS 2013

Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2014-06-24Bibliographically approved
In thesis
1. Trust-Based User Profiling
Open this publication in new window or tab >>Trust-Based User Profiling
2013 (English)Doctoral 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.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. xi, 48 p.
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 13:10
trust, userprofiling, userprofiles, privacy, interest, socialnetwork, recommendersystems
National Category
Information Systems
urn:nbn:se:kth:diva-118488 (URN)978-91-7501-651-1 (ISBN)
Public defence
2013-03-08, C1 Sal, Electrum, ICT/KTH, Isafjordsgatan 20, Kista, 13:00 (English)

QC 20130219

Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2014-01-24Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Dokoohaki, Nima
By organisation
Software and Computer systems, SCS
Information Systems

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 113 hits
ReferencesLink to record
Permanent link

Direct link