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20172024
most citedComparison-based Conversational Recommender System with Relative Bandit Feedback

40 citations · 125 across the 25 of their papers we have counts for

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5 papers · 1 filter

cs.IR2023

Disentangled Counterfactual Reasoning for Unbiased Sequential Recommendation

Yi Ren, Xu Zhao, Hongyan Tang +1

Sequential recommender systems have achieved state-of-the-art recommendation performance by modeling the sequential dynamics of user activities. However, in most recommendation sce…

cs.IR2022

Hierarchical Conversational Preference Elicitation with Bandit Feedback

Jinhang Zuo, Songwen Hu, Tong Yu +3

The recent advances of conversational recommendations provide a promising way to efficiently elicit users' preferences via conversational interactions. To achieve this, the recomme…

cs.IR2022★ 40 cited

Comparison-based Conversational Recommender System with Relative Bandit Feedback

Zhihui Xie, Tong Yu, Canzhe Zhao +1

With the recent advances of conversational recommendations, the recommender system is able to actively and dynamically elicit user preference via conversational interactions. To ac…

cs.IR2022★ 1 cited

A Graph-Enhanced Click Model for Web Search

Jianghao Lin, Weiwen Liu, Xinyi Dai +6

To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback. Most traditional click models…

cs.IR2021★ 32 cited

An Adversarial Imitation Click Model for Information Retrieval

Xinyi Dai, Jianghao Lin, Weinan Zhang +7

Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how use…