61 citations · 201 across the 37 of their papers we have counts for
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cs.IR2021★ 2 cited
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations
Yu Song, Jianxun Lian, Shuai Sun +4
User interest exploration is an important and challenging topic in recommender systems, which alleviates the closed-loop effects between recommendation models and user-item interac…
cs.IR2021
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
Junhan Yang, Zheng Liu, Bowen Jin +7
Transformer encoding networks have been proved to be a powerful tool of understanding natural languages. They are playing a critical role in native ads service, which facilitates t…
cs.IR2021
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
Jianxun Lian, Iyad Batal, Zheng Liu +4
Precise user modeling is critical for online personalized recommendation services. Generally, users' interests are diverse and are not limited to a single aspect, which is particul…