Kaiyuan Li

Multi-Agent RL-based Information Selection Model for Sequential Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

For sequential recommender, the coarse-grained yet sparse sequential signals mined from massive user-item interactions have become the bottleneck to further improve the recommendation performance. To alleviate the spareness problem, exploiting auxiliary semantic features (\eg textual …

CHIME: A Compressive Framework for Holistic Interest Modeling

2025 · arXiv (Cornell University)

Modeling holistic user interests is important for improving recommendation systems but is challenged by high computational cost and difficulty in handling diverse information with full behavior context. Existing search-based methods might lose critical signals during …

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

2021

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation …