Large Scale Hierarchical User Interest Modeling for Click-through Rate Prediction

With the explosive growth of online information, recommender systems have emerged as indispensable tools for navigating the complexities of content generation, discovery and consumption. The RecSys 2024 Challenge, organized by Ekstra Bladet, provides a comprehensive dataset and a robust news recommendation evaluation framework for tackling multifaceted challenges including modeling user preferences based on implicit behavior, accounting for the influence of the news agenda on user interests, and managing the rapid decay of news items. In this paper, we propose a novel Hierarchical User Interest Modeling (HUIM) approach to this challenge, leveraging both long-term invariant interests and short-term rapidly changing interests. Specifically, we utilize the multimodal representations along with the side information of items to distill the long-standing interests from user historical behaviors. By analyzing real-time context and user behavioral path patterns, we identify their fine-grained instant interests. Finally, an interest fusion network is proposed to adaptively fuse the long-term and short-term interests by contrasting the query-aware fine-grained behaviors with query-level cross entropy loss. Our team, BlackPearl, achieved a score of 0.8815 and ranked 2nd place on the final leaderboard.