3 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…