Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation-Reference-Cited by-同舟云学术

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Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation

Published:2021-06-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
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Author:

Yang Hojin¹,Sanner Scott¹,Wu Ga²,Zhou Jin Peng¹

Affiliation:

1. University of Toronto

2. Borealis AI

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3450613.3456814

Reference38 articles.

1. Using Confidence Bounds for Exploitation-Exploration Trade-offs;Auer Peter;J. Mach. Learn. Res.,2002

2. Transparent, Scrutable and Explainable User Models for Personalized Recommendation

3. Charles Blundell Julien Cornebise K. Kavukcuoglu and Daan Wierstra. 2015. Weight Uncertainty in Neural Network. In ICML. Charles Blundell Julien Cornebise K. Kavukcuoglu and Daan Wierstra. 2015. Weight Uncertainty in Neural Network. In ICML.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Training with One2MultiSeq: CopyBART for social media keyphrase generation;The Journal of Supercomputing;2024-04-04

2. Y-Rank: A Multi-Feature-Based Keyphrase Extraction Method for Short Text;Applied Sciences;2024-03-16

3. Mitigating the Filter Bubble While Maintaining Relevance;Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval;2022-07-06

4. Distributional Contrastive Embedding for Clarification-based Conversational Critiquing;Proceedings of the ACM Web Conference 2022;2022-04-25

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