119 citations · 387 across the 14 of their papers we have counts for
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cs.IR2022★ 4 cited
When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation
Yu Tian, Jianxin Chang, Yannan Niu +2
Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history. Compared to conventional sequential models that leverage…
cs.IR2022★ 119 cited
Disentangling Long and Short-Term Interests for Recommendation
Yu Zheng, Chen Gao, Jianxin Chang +4
Modeling user's long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approach…
cs.IR2022★ 42 cited
A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
Zihua Si, Xueran Han, Xiao Zhang +4
Machine-learning based recommender systems(RSs) has become an effective means to help people automatically discover their interests. Existing models often represent the rich inform…