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Found 6 papers, 6 papers with code
Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System
Despite their effectiveness under cold scenarios, we observe that they underperform simple traditional collaborative filtering models under warm scenarios due to the lack of collaborative knowledge.
DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning
We investigate the replay buffer in rehearsal-based approaches for graph continual learning (GCL) methods.
MUSE: Music Recommender System with Shuffle Play Recommendation Enhancement
Recommender systems have become indispensable in music streaming services, enhancing user experiences by personalizing playlists and facilitating the serendipitous discovery of new music.
Task Relation-aware Continual User Representation Learning
However, since learning task-specific user representations for every task is infeasible, recent studies introduce the concept of universal user representation, which is a more generalized representation of a user that is relevant to a variety of tasks.
Shift-Robust Molecular Relational Learning with Causal Substructure
To do so, we first assume a causal relationship based on the domain knowledge of molecular sciences and construct a structural causal model (SCM) that reveals the relationship between variables.
Heterogeneous Graph Learning for Multi-modal Medical Data Analysis
In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data.
