Improving the Trustworthiness of Recommendations in Collaborative Filtering under the Belief Function Framework

Abstract

Collaborative Filtering (CF) consists of filtering data, predicting users' preferences and providing recommendations accordingly. Commonly, neighborhood-based CF methods predict the future ratings based on similar users (user-based) or similar items (item-based) to perform recommendations. However, the reliability of the information provided by these pieces of evidence as well as the final predictions cannot be fully trusted. Incorporating trust in the recommendation process can be argued to be an important challenge in Recommender Systems (RSs). To tackle these issues, we propose new CF approaches under the belief function framework. The final prediction is obtained by fusing evidences from similar items or similar users using Dempster's rule of combination. The prediction process of our evidential approaches is able to provide the users with a global overview of their possible preferences. This would lead to increase their confidence towards the system as well as their satisfaction. In this paper, we mainly highlight the benefits of incorporating uncertainty in CF approaches using the belief function theory. We present the preliminary results and also discuss our ongoing works, as well as the challenges in the future.