A Bird's-eye View of Reranking: from List Level to Page Level

• Published in: arXiv.org 2022-11
• Main Authors: Yunjia Xi, Lin, Jianghao, Liu, Weiwen, Dai, Xinyi, Zhang, Weinan, Zhang, Rui, Tang, Ruiming, Yu, Yong
• Format: Article
• Language: English
• Subjects: Datasets; Modules; Multimedia; Recommender systems; User behavior
• ISSN: 2331-8422

Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface design, the recommendation page has evolved to a multi-list style. Separately employing traditional list-level reranking methods for different lists overlooks the inter-list interactions and the effect of different page formats, thus yielding suboptimal reranking performance. Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists. To this end, we propose to draw a bird's-eye view of \textbf{page-level reranking} and design a novel Page-level Attentional Reranking (PAR) model. We introduce a hierarchical dual-side attention module to extract personalized intra- and inter-list interactions. A spatial-scaled attention network is devised to integrate the spatial relationship into pairwise item influences, which explicitly models the page format. The multi-gated mixture-of-experts module is further applied to capture the commonalities and differences of user behaviors between different lists. Extensive experiments on a public dataset and a proprietary dataset show that PAR significantly outperforms existing baseline models.