NNIA-RS: A multi-objective optimization based recommender system

Bingrui Geng, Lingling Li, Licheng Jiao, Maoguo Gong, Qing Cai and Yue Wu

Physica A: Statistical Mechanics and its Applications, 2015, vol. 424, issue C, 383-397

Abstract: With the rapid development of the Internet, we have entered an era of information explosion. In this kind of situation, recommender systems appear. It is meaningful that recommender systems help people to get useful and valuable items from massive data (e.g. movies, music, books, jokes). The traditional recommender systems especially collaborative filtering make recommendations based on similarity between items or users. Under this strategy, the similarities between the items in the recommendation list are extremely high so as to guarantee the accuracy of a recommendation, but it easily results in the loss of diversity. Actually, the task of recommender systems can be modeled as a multi-objective optimization problem. By taking into account of the recommendation accuracy and diversity, a multi-objective evolutionary algorithm for recommender systems is proposed. Each run of the proposed algorithm can produce a set of nondominated solutions. Each solution denotes a unique recommendation list to a target user. Experiments demonstrate that the proposed algorithm is promising and effective in terms of recommendation diversity and novelty.

Keywords: Recommender systems; Multi-objective optimization; Accuracy; Diversity; Novelty (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:424:y:2015:i:c:p:383-397

DOI: 10.1016/j.physa.2015.01.007

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