Prima: Probabilistic Ranking with Inter-Item Competition and Multi-Attribute Utility Function

摘要

This paper proposes PRIMA: Probabilistic Ranking with Inter-item competition and Multi-Attribute utility function, which ranks items based on their probabilities of being a user's best choice. This framework is particularly important in E-commerce applications for making recommendations, predicting sales, and developing pricing strategies. To achieve mathematical tractability, it uses the weight-based multi-attribute utility function to address the inter-attribute tradeoff, where the weight reflects a user's personal preference for each attribute. The proposed work updates the weight from a user's past transactions using the concept of marginal rate of substitution from microeconomics, addresses the interitem competition, and computes the items' probabilities of being a user's best choice. Real user test results show that the proposed framework achieves comparable ranking accuracy to the state-of-the-art work with significant improvements in model simplicity and mathematical tractability.