98 citations · 116 across the 9 of their papers we have counts for
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cs.IR2023★ 98 cited
Collaborative Large Language Model for Recommender Systems
Yaochen Zhu, Liang Wu, Qi Guo +2
Recently, there has been growing interest in developing the next-generation recommender systems (RSs) based on pretrained large language models (LLMs). However, the semantic gap be…
cs.IR2023★ 10 cited
Path-Specific Counterfactual Fairness for Recommender Systems
Yaochen Zhu, Jing Ma, Liang Wu +3
Recommender systems (RSs) have become an indispensable part of online platforms. With the growing concerns of algorithmic fairness, RSs are not only expected to deliver high-qualit…
cs.IR2023★ 1 cited
When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?
Yushun Dong, Jundong Li, Tobias Schnabel
In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation. Nevertheless, multiple recent studies have revealed that the r…