@article{Liu_Xiao_Wu_Miao_Zhang_Zhao_Tang_2020, title={Diversified Interactive Recommendation with Implicit Feedback}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5931}, DOI={10.1609/aaai.v34i04.5931}, abstractNote={<p>Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attention. 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 <span style="text-decoration: underline;">D</span>iversified <span style="text-decoration: underline;">C</span>ontextual <span style="text-decoration: underline;">C</span>ombinatorial <span style="text-decoration: underline;">B</span>andit (DC<sup>2</sup>B), for interactive recommendation with users’ implicit feedback. Specifically, DC<sup>2</sup>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<sup>2</sup>B. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed method in balancing the recommendation accuracy and diversity.</p>}, number={04}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Liu, Yong and Xiao, Yingtai and Wu, Qiong and Miao, Chunyan and Zhang, Juyong and Zhao, Binqiang and Tang, Haihong}, year={2020}, month={Apr.}, pages={4932-4939} }