Eliciting Trust Values from Recommendation Errors

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Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user’s information space to suit their particular information needs. Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users’ tastes and opinions. Our work focusses on these similarities and the fact that current techniques for defining which users contribute to recommendation are in need of improvement. In this paper we propose the use of trustworthiness as an improvement to this situation. In particular, we define and empirically test a technique for eliciting trust values for each producer of a recommendation based on that user’s history of contributions to recommendations. We present three computational models for leveraging under/ overestimate errors in users’ past contributions to recommendations to generate a range each side of a fixed point on the recommendation scale to be presented to the target user. We show how this trust-based technique can be easily incorporated into a standard collaborative filtering algorithm and define a fair comparison in which our technique outperforms a benchmark algorithm in predictive accuracy.