NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems
Shuli Wang, Xue Wei, S. C. Kou, Chi Wang, W. Chen, Qi Tang, Yinhua Zhu, Xiong Xiao, Xingxing Wang · 2025
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.