Interactive collaborative filtering

By Xiaoxue Zhao, Weinan Zhang, Jun Wang. 2013

191 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Fuli Feng
5. Chongming Gao
6. Wenqiang Lei
7. Le Wu
8. Meng Wang

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, background): “Recently, some people try to combine bandit with clustering based collaborative filtering [14], andmatrix factorization [6, 21, 32, 42, 43, 51], in order to model more complex user and item relationship, and utilize the social network relationship in determining the reward function.”
• Factorization Bandits for Interactive Recommendation (2017, methodology): “…these two components are integrated in an ad-hoc manner: both contextual and context-free bandits have been explored on top of various factorization methods (Zhao, Zhang, and Wang 2013; Nakamura 2014; Kawale et al. 2015), given they only provide an index of candidate items for feedback acquisition.”
• Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023, unknown): “To enhance the readability, we make some modifications to the original instructions that are used in our experiments.”
• Personalized Recommendation via Parameter-Free Contextual Bandits (2015, unknown): “promising performance in many empirical studies [13, 14, 29, 36].”
• Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Reinforcement learning [13], [144], [145], [146], [147], [148], [149], [150], [151], [152], [153], [154], [155], [156], [156], [157], [158], [159], [160] Others [161], [162]”
• Learning Hidden Features for Contextual Bandits (2016, methodology): “Some recent empirical studies combined bandit algorithms with factorization-based methods for online collaborative filtering [27, 12].”
• Streaming Recommender Systems (2016, methodology): “[27] utilize a selective sampling scheme under the PMF framework [18] to achieve interactive updates.”
• Towards Conversational Recommender Systems (2016, methodology): “In contrast, recent work has moved towards continuously learning recommenders [4, 16, 33].”
• A Survey on Conversational Recommender Systems (2020, methodology): “ion task, e.g., the end-to-end learning approaches presented in [151] and [74], which focus on restaurant search and movie-ticket booking, respectively. Furthermore, we found a few works like [49] or [166], which use the term “interactive recommendation”, which however rather refers to a system that”
• Advances and Challenges in Conversational Recommender Systems: A Survey (2021, methodology): “Multi-Armed Bandits in CRSs As the classic algorithm for E&E trade-offs, MAB-based models can be seamlessly plugged into the online recommendation setting [203, 215], interactive recommendation [214, 176], and CRSs [32, 206, 95].”
• Efficient Thompson Sampling for Online Matrix-Factorization Recommendation (2015, methodology): “[2].”
• A UCB-Like Strategy of Collaborative Filtering (2014, methodology): “The method using Thompson sampling proposed by Zhao et al. (2013) is not deterministic method that was used to select items for a user as a solution of the new user problem.”