Towards more trustworthy predictions: A hybrid evidential movie recommender system

Recommender Systems (RSs) are considered as popular tools that have revolutionized the e-commerce and digital marketing. Their main goal is predicting the users’ future preferences and providing accessible and personalized recommendations. However, uncertainty can spread at any level throughout the recommendation process, which may affect the results. In fact, the ratings given by the users are often unreliable. The final provided predictions itself may also be pervaded with uncertainty and doubt. Obviously, the reliability of the predictions cannot be fully certain and trustworthy. For the system to be effective, recommendations must inspire trust in the system and provide reliable and credible recommendations. The user may speculate about the uncertainty pervaded behind the given recommendation. He could tend to a reliable recommendation offering him a global overview about his preferences rather than an inappropriate one that contradicts his activities and objectives. While such imperfection cannot be ignored, traditional RSs are rarely able to deal with the uncertainty spreading around the prediction process, which may affect the credibility, the transparency and the trustworthiness of the current RS. Thus, in this paper, we opt for the uncertain framework of the belief function theory (BFT), which allows us to represent, quantify and manage imperfect evidence. By using the BFT, the users’ preferences and the interactions between the neighbors can be represented under uncertainty. Evidence from different information sources can then be combined leading to more reliable results. The proposed approach is a hybrid evidential movie RS that uses different data sources and delivers a personalized user-interface allowing a global overview of the possible future preferences. This representation would increase the users’ confidence towards the system as well as their satisfaction. Experiments are performed on MovieLens and their additional features provided by the Internet Movie Database (IMDb) and Rotten Tomatoes. The new approach achieves promising results compared to traditional approaches in terms of MAE, NMAE and RMSE. It also reaches interesting Precision, Recall and F-measure values of respectively, 0.782, 0.792 and 0.787.