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General Information
    • ISSN: 2382-6185
    • Frequency: Quarterly (2015-2016); semiyearly (Since 2017)
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, DOAJ, Engineering & Technology Digital Library, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Editor-in-chief
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2015 Vol.1(1): 9-17 ISSN: 2382-6185
DOI: 10.7763/IJKE.2015.V1.2

Improving Recommendation Using Trust and Sentiment Inference from OSNs

Dimah Alahmadi and Xaio-Jun Zeng
Abstract—Recommender systems (RSs) provide personalised suggestions of information or products relevant to users’ needs. Although RSs have made substantial progresses in theory and algorithm development and have achieved many commercial successes, how to utilise the widely available information in Online Social Networks (OSNs) has been largely overlooked. Noticing such a gap in the existing research in RSs and taking into account a user’s selection being greatly influenced by his/her trusted friends and their opinions, this paper proposes a framework of Implicit Social Trust and Sentiment (ISTS) based RSs, which improves the existing recommendation approaches by exploring a new source of data from friends’ short posts in microbloggings as micro-reviews. The impact degree of friends’ sentiment and level being trusted to a user’s selection are identified by using machine learning methods including Naive Bayes, Logistic Regression and Decision Trees. As the verification of the proposed framework, experiments using real social data from Twitter microblogger are presented and results show the effectiveness and promising of the proposed approach.

Index Terms—Recommender systems, machine learning, trust, sentiment analysis, microblogging.

The authors are with School of Computer Science, The University of Manchester, Manchester, M13 9PL, UK (e-mail: dimah.alahmadi@postgrad.manchester.ac.uk, x.zeng@manchester.ac.uk).

[PDF]

Cite: Dimah Alahmadi and Xaio-Jun Zeng, "Improving Recommendation Using Trust and Sentiment Inference from OSNs," International Journal of Knowledge Engineering vol. 1, no. 1, pp. 9-17, 2015.

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