3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.IR2026
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…
cs.IR2026
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…
cs.IR2025★ 3 cited
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…