Fine-grained modeling of user interests for sequential recommendation
麒 张, 宾 吴, 中川 孙, 阳东 叶 · Scientia Sinica Informationis · 2021
In recent years, the sequential recommendation method has attracted extensive attention owing to its practicability and high accuracy. Different from general recommendations, explicitly capturing short-term interests based on users' recent actions lies at the heart of a sequential recommender. Existing methods have either modeled successive items step by step without considering the many-to-one relationship or neglected the local-order information, considering the recent items as a set. This neither fully mines complex item transitions nor depicts the evolution of user interests. Therefore, we propose the gating coupled capsule network (GCC), a fine-grained analysis method to model individual-level, union-level, and local-order relationships of short-term user interests. Specifically, we designed a user-specific capsule module to capture high-level temporal interaction sequences, which model union-level patterns and perceive local-order relations. Moreover, we present a personalized gating module to focus on the pairwise relationships among items to capture the influence of individual-level information on short-term user interests. Extensive experiments under four real-world recommendation scenarios demonstrate that GCC outperforms the state-of-the-art methods on different ranking metrics.