Enhanced pareto multi-objective artificial bee colony optimization for collaborative recommender system - Vimala, S.V.; Vivekanandan, K. (International Journal of Engineering and Technology, 2018)
Recommender systems (RS) are systems that filter information and help users to choose products from a large amount of information available online. RS recommend satisfactory and useful products (items) like movies, music, books, and jokes to target users that they are interested in. The majority of traditional recommendation algorithms mainly concentrate on improving the performance accuracy; thus, these algorithms tend to suggest only popular items. Furthermore, diversity is another important non accuracy metric for personalized recommendations to suggest unusual or different items. To balance the conflict between accuracy and diversity, multi-objective optimization algorithms are used, which maximize these conflicting metrics simultaneously. The present article proposes an enhanced Pareto multi-objective artificial bee colony optimization algorithm for collaborative recommendation systems (EPMABC-RS). Artificial bee colony optimization is performed using the crossover operator to exchange useful information for improving local search. Important data are fully exploited, and the algorithm is expected to converge rapidly and give more accurate recommendation results. The proposed algorithm optimizes the two objective functions simultaneously and gives a set of solutions, in which no solution dominates the other in the set. Each solution suggests a distinct recommendation result to users. Decision makers can choose a recommendation according to their requirements. The findings reveal that the EPMABC algorithm is more effective in providing a set of different recommendation results with accuracy and diversity of items for the target user.