Abstract
Distrust based recommender systems have drawn much more attention and became widely acceptable in recent years. Previous works have investigated using trust information to establish better models for rating prediction, but there is a lack of methods using distrust relations to derive more accurate ranking-based models. In this article, we develop a novel model, named TNDBPR (Trust Neutral Distrust Bayesian Personalized Ranking), which simultaneously leverages trust, distrust, and neutral relations for item ranking. The experimental results on Epinions dataset suggest that TNDBPR by leveraging trust and distrust relations can substantially increase various performance evaluations including F1 score, AUC, Precision, Recall, and NDCG.
Highlights
Recommendation tasks have been divided into two types: rating prediction and item ranking
In the Epinions dataset, the users’ social relations include “trust relations” that user pairs with positive ratings, “distrust relations” that user pairs with negative ratings, and “neutral relations” that user pairs without any ratings
We evaluated the performance of the proposed TNDBPR for different numbers of Top-N recommendations
Summary
Recommendation tasks have been divided into two types: rating prediction and item ranking. Social relations refer to trust relations as usual and include distrust relations In this regard, if we would like to recommend items to a user, we should understand what she prefers as well as what she dislikes. Many works have been proposed to leverage social relations for rating prediction tasks [3,4,5,6], For example, Bharadwaj et al [7] proposed a collaborative filtering model based on user trust computation. TNDBPR incorporates users’ trust, neutral, and distrust relations to better predict users’ preference and disgust, thereby boosting the performance of item recommendation. To the best of our knowledge, it is the first work incorporating distrust relations to evaluate users’ item ranking preference.
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