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cs.IR2026
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…
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…