NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems

TL;DR

This paper introduces NLGR, a neighbor list-based generative reranking model that improves personalized recommendation quality by addressing local optimality issues in list generation, validated through extensive experiments and deployment.

Contribution

NLGR proposes a novel neighbor list approach and a non-autoregressive sampling method to enhance generator training and list optimization in recommender systems.

Findings

NLGR outperforms existing reranking methods on public datasets.

NLGR achieves significant improvements in recommendation accuracy.

Successfully deployed NLGR on Meituan food delivery platform.

Abstract

Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list. Due to the inherent challenges of combinatorial search spaces, some current research adopts an evaluator-generator paradigm, with a generator generating feasible sequences and an evaluator selecting the best sequence based on the estimated list utility. However, these methods still face two issues. Firstly, due to the goal inconsistency problem between the evaluator and generator, the generator tends to fit the local optimal solution of exposure distribution rather than combinatorial space optimization. Secondly, the strategy of generating target items one by one is difficult to achieve optimality because it ignores the information of subsequent items. To address these issues, we propose a utilizing Neighbor Lists model for Generative Reranking (NLGR), which aims to improve the performance of the generator in the combinatorial space. NLGR follows the evaluator-generator paradigm and improves the generator's training and generating methods. Specifically, we use neighbor lists in combination space to enhance the training process, making the generator perceive the relative scores and find the optimization direction. Furthermore, we propose a novel sampling-based non-autoregressive generation method, which allows the generator to jump flexibly from the current list to any neighbor list. Extensive experiments on public and industrial datasets validate NLGR's effectiveness and we have successfully deployed NLGR on the Meituan food delivery platform.