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Yang Bao

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3 papers

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AAAI Conference 2014 Conference Paper

Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation

Hui Fang
Yang Bao
Jie Zhang

Trust has been used to replace or complement ratingbased similarity in recommender systems, to improve the accuracy of rating prediction. However, people trusting each other may not always share similar preferences. In this paper, we try to fill in this gap by decomposing the original single-aspect trust information into four general trust aspects, i. e. benevolence, integrity, competence, and predictability, and further employing the support vector regression technique to incorporate them into the probabilistic matrix factorization model for rating prediction in recommender systems. Experimental results on four datasets demonstrate the superiority of our method over the state-of-the-art approaches.

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AAAI Conference 2014 Conference Paper

TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation

Yang Bao
Hui Fang
Jie Zhang

Although users’ preference is semantically reflected in the free-form review texts, this wealth of information was not fully exploited for learning recommender models. Specifically, almost all existing recommendation algorithms only exploit rating scores in order to find users’ preference, but ignore the review texts accompanied with rating information. In this paper, we propose a novel matrix factorization model (called TopicMF) which simultaneously considers the ratings and accompanied review texts. Experimental results on 22 real-world datasets show the superiority of our model over the state-of-the-art models, demonstrating its effectiveness for recommendation tasks.

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IJCAI Conference 2013 Conference Paper

Misleading Opinions Provided by Advisors: Dishonesty or Subjectivity

Hui Fang
Yang Bao
Jie Zhang

It is indispensable for users to evaluate the trustworthiness of other users (referred to as advisors), to cope with possible misleading opinions provided by them. Advisors’ misleading opinions may be induced by their dishonesty, subjectivity difference with users, or both. Existing approaches do not well distinguish the two different causes. In this paper, we propose a novel probabilistic graphical trust model to separately consider these two factors, involving three types of latent variables: benevolence, integrity and competence of advisors, trust propensity of users, and subjectivity difference between users and advisors. Experimental results on real datasets demonstrate that our method advances state-of-the-art approaches to a large extent.

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