Towards comprehensive approaches for the rating prediction phase in memory-based collaborative filtering recommender systems

• Published in: Information sciences Vol. 589; pp. 878 - 910
• Main Author: Nam, Le Nguyen Hoai
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.04.2022
• Subjects: Collaborative filtering; Rating prediction; Recommender systems; Collaborative filtering; Recommender systems; Rating prediction
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2021.12.123

•We propose a new rating prediction method in memory-based collaborative filtering.•We aim at the weights that are estimated in detail for each ordered triple (the neighbor, the active user, the target item).•We effectively model the fact that a neighbor of the active user did not rate the target item.•We implement the proposed approach for the combination of user-based and item-based views. Recommender systems play an indispensable role in today’s online businesses. In these systems, memory-based (neighborhood-based) collaborative filtering is an important strategy to predict items as expected by users. It consists of two phases: computing the preference similarity between each pair of users in the offline phase and predicting the rating of an active user for a target item in the online phase by aggregating ratings of his/her neighbors for the target item. Previous studies on memory-based collaborative filtering have heavily concentrated on proposing methods for the computation of user preference similarity. To further improve the performance of memory-based collaborative filtering, this paper is aimed at the rating prediction phase. By optimizing a proposed objective function, the method we used in the rating prediction phase helps more accurately estimate the weight between the active user and each of his/her neighbors. The experimental results show that the proposed method outperforms others, especially in the case of a small and medium number of selected neighbors.