Revisit Recommender System in the Permutation Prospective

Abstract

Recommender systems (RS) work effective at alleviating information overload and matching user interests in various web-scale applications. Most RS retrieve the user's favorite candidates and then rank them by the rating scores in the greedy manner. In the permutation prospective, however, current RS come to reveal the following two limitations: 1) They neglect addressing the permutation-variant influence within the recommended results; 2) Permutation consideration extends the latent solution space exponentially, and current RS lack the ability to evaluate the permutations. Both drive RS away from the permutation-optimal recommended results and better user experience. To approximate the permutation-optimal recommended results effectively and efficiently, we propose a novel permutation-wise framework PRS in the re-ranking stage of RS, which consists of Permutation-Matching (PMatch) and Permutation-Ranking (PRank) stages successively. Specifically, the PMatch stage is designed to obtain the candidate list set, where we propose the FPSA algorithm to generate multiple candidate lists via the permutation-wise and goal-oriented beam search algorithm. Afterwards, for the candidate list set, the PRank stage provides a unified permutation-wise ranking criterion named LR metric, which is calculated by the rating scores of elaborately designed permutation-wise model DPWN. Finally, the list with the highest LR score is recommended to the user. Empirical results show that PRS consistently and significantly outperforms state-of-the-art methods. Moreover, PRS has achieved a performance improvement of 11.0% on PV metric and 8.7% on IPV metric after the successful deployment in one popular recommendation scenario of Taobao application.

Under the review of the KDD2021 Applied Data Science track

References in corpus (6)

• Sequence-to-Sequence Learning as Beam-Search Optimization
• Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
• DeepMDP: Learning Continuous Latent Space Models for Representation Learning
• ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
• Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
• Large Scale Product Graph Construction for Recommendation in E-commerce

Cited by in corpus (2)

• Context-aware Reranking with Utility Maximization for Recommendation
• YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker