3 citations · 3 across the 5 of their papers we have counts for
5 papers
MIDA: Multi-Interest Decoding with Adaptive Aggregation for Session-based Recommendation
Linjiang Guo, Nitin Bisht, Shiqing Wu +1
Session-based recommendation (SBR) infers user interest from limited interaction sequences, yet current models show a consistent performance drop when longer histories are availabl…
TRUST: Item-Calibrated Interval Evidence for Temporal Session-Based Recommendation
Linjiang Guo, Nitin Bisht, Shiqing Wu +2
Temporal signals have been widely used in session-based recommendation to infer user interest. Existing temporal session-based recommenders primarily rely on absolute interval valu…
S2-CAR: Segmentation-Supervised Complexity-Adaptive Recommendation
Linjiang Guo, Nitin Bisht, Shiqing Wu +2
Sequential recommendation aims to predict user preferences from interaction histories, yet existing models often struggle when behavior patterns become complex and heterogeneous. A…
Cross-Domain Molecular Relational Learning: Leveraging Chemical Structure-Activity Analysis
Peiliang Zhang, Jingling Yuan, Shiqing Wu +4
Recent advances in molecular representation integrates molecular topological and visual modalities, opening new avenues for precise Molecular Relational Learning (MRL). Existing MR…
Refining Contrastive Learning and Homography Relations for Multi-Modal Recommendation
Shouxing Ma, Yawen Zeng, Shiqing Wu +1
Multi-modal recommender system focuses on utilizing rich modal information ( i.e., images and textual descriptions) of items to improve recommendation performance. The current meth…