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cs.IR2022
Improving Micro-video Recommendation via Contrastive Multiple Interests
Beibei Li, Beihong Jin, Jiageng Song +3
With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more a…
cs.IR2021
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval
Hongyin Tang, Xingwu Sun, Beihong Jin +3
Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than tradit…
cs.IR2021
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks
Xinzhou Dong, Beihong Jin, Wei Zhuo +2
Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of ite…