Weiwei Yuan
ABSTRACT
Existing models of the Trust-Aware Recommender System (TARS) build personalized trust networks for the active users to predict ratings. These models have reasonable rating prediction performances, while suffer from high computational complexity. One solution is to utilize the global rating prediction mechanism for TARS, in which an intuitive assumption is that more reputable recommenders give more accurate recommendations. In addition, due to the scale-freeness of the trust network, some users have and continuously have superior reputations than others. However, we show via comprehensive experiments on the real TARS data that the recommendations given by recommenders with higher reputations do not tend to be more accurate. Furthermore, even the recommendations given by the recommenders with superior high reputations do not tend to more accurate. Our experimental study provides promising directions for the future research on the rating prediction mechanism of TARS.
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Digital Library
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- Yuan, W., Guan, D., Lee, Y. K., and Lee, S. The Small-World Trust Network. Applied Intelligence (2010): 1--12, April 27, 2010. Google ScholarDigital Library
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Index Terms
- More reputable recommenders give more accurate recommendations?
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Reviews
Amrinder Arora
Predictive analytics has hit the mainstream, thanks to the emergence of many day-to-day consumer applications such as Pandora, Netflix, Yelp, and Epinions, all of which contain some variation of a recommendation engine. These engines (or systems) recommend items about which the user is more likely to have a favorable opinion. A special class of recommendation systems is the one in which a user likes (trusts) a reviewer. These recommendation systems then use the network of trust to predict what items the user may like. Such systems attain reasonable rating predictions, albeit at a cost of high computational complexity. In this paper, the authors seek to reduce the computational complexity of trust-aware recommendation systems (TARS). They hypothesize that we can perhaps reach a reasonable level of prediction accuracy by focusing on reputable recommenders alone. They define reputation by the distribution of the reviewer's recommendations being liked or trusted. The authors conclude that while the hypothesis leads to a much more efficient algorithm, the accuracy of recommendations is compromised greatly, thereby requiring the summary rejection of the hypothesis. The results are supported by extensive experiments on Epinions datasets consisting of many thousands of users and almost half a million trust relations. For research such as this, in which the authors try an alternative and report that the alternative method does not work, the value comes from our enriched understanding of the system model and the relationships between various components. Trust-aware recommendation systems are part of the family of recommendation systems that do not use the content of the item in the prediction. Rather, they use the similarity between the users and their mutual trust or liking factor to guide the ratings. That observation by itself can be interpreted in two contrasting ways: (1) a user who is trusted (directly or indirectly) by many others can generate more useful ratings, and (2) the reputation of a recommender plays no role for a user who does not like or trust the recommender. From this paper, it appears that the second interpretation is the more correct one, at least for the scenarios and datasets considered in this work. However, it may be interesting to consider the problem of the characterization of system models in which the trust "carries over," and those in which it doesn't. Online Computing Reviews Service
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