4 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…
Coronavirus statistics causes emotional bias: a social media text mining perspective
Linjiang Guo, Zijian Feng, Yuxue Chi +2
While COVID-19 has impacted humans for a long time, people search the web for pandemic-related information, causing anxiety. From a theoretic perspective, previous studies have con…