Towards personalized recommendation with enhancing preference matching through scene-weighted reranking

Reranking is crucial in recommendation systems, refining candidate lists to significantly enhance the matching of user preferences and encourage engagement. While existing algorithms often focus solely on pairwise item interactions, they overlook local connections within item subsets. To address this limitation, we introduce the concept of “scenes” to explicitly mine local relationships among multiple items within a list, representing inter-scene correlations through undirected graphs. To effectively integrate these scenes and address the challenge of scoring items that cannot be definitively categorized into a single scene, we propose a scene-weighted reranking algorithm. This novel approach computes a final item score by leveraging scene-user preference matching scores, weighted by item-scene similarities. Experimental results demonstrate that compared to existing methods, our algorithm achieves more accurate item rankings that better reflect users’ true preferences, ultimately providing higher-quality recommendation sequences. This research contributes to the field by offering a more nuanced approach to capturing both local and global item relationships, specifically enhancing preference matching in personalized recommendation.