A multi-objective artificial bee colony approach for profit-aware recommender systems

Movie recommender systems are increasingly present in our daily lives, offering content of interest from streaming providers. Objectives in addition to the liking probability can be proposed to provide movie recommendations. However, there is a lack of recommenders that are aware of the benefit and that address the multi-objective nature of the problem. A profit-aware recommender system based on swarm intelligence in a multi-objective environment (multi-objective artificial bee colony, MOABC) has been designed, imple-mented, and applied. The proposed approach incorporates new intelligent operators that try to improve both objectives (liking probability and profit) simultaneously in each itera-tion instead of exclusively using randomness. This increases the quality of the recommen-dations with respect to the state-of-the-art algorithms. This new proposal has been evaluated using MovieLens datasets, covering different sizes (large, medium, and small). The experiments show that the MOABC performs better than collaborative filtering (CF, a standard in recommender systems) and Non-dominated Sorting Genetic Algorithm II (NSGA-II, the only multi-objective proposal in scientific literature that is profit aware) in terms of accuracy and global profit. Furthermore, statistical analysis shows that the pro-posed approach generates better and more robust results, also showing that the multi -objective nature of the problem must be exploited.(c) 2023 Elsevier Inc. All rights reserved.

Automated summary

Movie recommender systems often focus on a single objective, but this study presents a multi-objective recommender system that considers both liking probability and profit simultaneously. The proposed system outperforms existing algorithms in terms of accuracy and global profit, based on evaluations using MovieLens datasets.