Bandit Learning for Diversified Interactive Recommendation
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| Titel: | Bandit Learning for Diversified Interactive Recommendation |
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| Autoren: | Liu, Yong, Xiao, Yingtai, Wu, Qiong, Miao, Chunyan, Zhang, Juyong |
| Verlagsinformationen: | 2019-06-30 |
| Publikationsart: | Electronic Resource |
| Abstract: | Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attentions. Previous methods mainly focus on optimizing recommendation accuracy. However, they usually ignore the diversity of the recommendation results, thus usually results in unsatisfying user experiences. In this paper, we propose a novel diversified recommendation model, named Diversified Contextual Combinatorial Bandit (DC$^2$B), for interactive recommendation with users' implicit feedback. Specifically, DC$^2$B employs determinantal point process in the recommendation procedure to promote diversity of the recommendation results. To learn the model parameters, a Thompson sampling-type algorithm based on variational Bayesian inference is proposed. In addition, theoretical regret analysis is also provided to guarantee the performance of DC$^2$B. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed method. |
| Index Begriffe: | Computer Science - Information Retrieval, Computer Science - Machine Learning, Statistics - Machine Learning, text |
| URL: | |
| Verfügbarkeit: | Open access content. Open access content |
| Other Numbers: | COO oai:arXiv.org:1907.01647 1228355261 |
| Originalquelle: | CORNELL UNIV From OAIster®, provided by the OCLC Cooperative. |
| Dokumentencode: | edsoai.on1228355261 |
| Datenbank: | OAIster |
Nájsť tento článok vo Web of Science | Abstract: | Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attentions. Previous methods mainly focus on optimizing recommendation accuracy. However, they usually ignore the diversity of the recommendation results, thus usually results in unsatisfying user experiences. In this paper, we propose a novel diversified recommendation model, named Diversified Contextual Combinatorial Bandit (DC$^2$B), for interactive recommendation with users' implicit feedback. Specifically, DC$^2$B employs determinantal point process in the recommendation procedure to promote diversity of the recommendation results. To learn the model parameters, a Thompson sampling-type algorithm based on variational Bayesian inference is proposed. In addition, theoretical regret analysis is also provided to guarantee the performance of DC$^2$B. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed method. |
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