_:n6921723ffdd64b259fcfb202929b506axb67
<http://schema.org/description>
"Interactive recommendation that models the explicit interactions between\nusers and the recommender system has attracted a lot of research attentions in\nrecent years. Most previous interactive recommendation systems only focus on\noptimizing recommendation accuracy while overlooking other important aspects of\nrecommendation quality, such as the diversity of recommendation results. In\nthis paper, we propose a novel recommendation model, named\nDiversity-promoting Deep Reinforcement\nLearning (D^2RL), which encourages the diversity of\nrecommendation results in interaction recommendations. More specifically, we\nadopt a Determinantal Point Process (DPP) model to generate diverse, while\nrelevant item recommendations. A personalized DPP kernel matrix is maintained\nfor each user, which is constructed from two parts: a fixed similarity matrix\ncapturing item-item similarity, and the relevance of items dynamically learnt\nthrough an actor-critic reinforcement learning framework. We performed\nextensive offline experiments as well as simulated online experiments with real\nworld datasets to demonstrate the effectiveness of the proposed model."
<https://deepai.org/profile/lizhen-cui>