An Efficient Approach for Rational Next-Basket Recommendation

Abstract

E-commerce is one of the most valuable and popular application scenarios of recommendation technology. In this context, making rational decisions is required due to the necessity of objectivity and decision rationale tracking. However, a significant number of irrational recommendations may be generated as a result of using a narrow range of interestingness criteria, considering inconsistent user preferences, overlooking the temporal weights of items, and using outlier choices in making a prediction. In this paper, we propose an efficient approach called MONBR (Multi-Objective Next-Basket Recommendation) to improve the quality of recommendations. An approach promotes rational recommendations by measuring the temporal importance of items to the user using several interestingness criteria, namely utility, popularity, stability, frequency, occupancy, and novelty. Moreover, combining the multi-criteria weights of items obtained from the MONBR approach with the item-based collaborative filtering technique results in more accurate and reasonable recommendations. Experiment results on real-world datasets demonstrate that the proposed approach improves the quality of recommendations. The average quality of recommendations derived from the proposed approach is up to 60.95% more accurate than state-of-the-art algorithms with a recommended basket size ranging from 10 to 100. Furthermore, the results highlight the importance of adapting objectivity and rationality in the recommendation process to meet the needs of as many users as possible. The average user-level performance is up to 10.21% higher on dense datasets.