Deep reinforcement learning for page-wise recommendations

By Xiangyu Zhao, Long Xia, L. Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang. ACM Conference on Recommender Systems, 2018

442 citations recorded by the source corpus.

Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is - users are recommended a page of items and provide feedback; and then the system recommends a new page of items. To effectively capture such interaction for recommendations, we need to solve two key problems - (1) how to update recommending strategy according to user's real-time feedback, and 2) how to generate a page of items with proper display, which pose tremendous challenges to traditional recommender systems. In this paper, we study the problem of page-wise recommendations aiming to address aforementioned two challenges simultaneously. In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5956c34032126185d8ad19695e4a1a191c08b5a1

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In Artificial Intelligence

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Reinforcement Learning based Recommender Systems: A Survey (2021, methodology): “In another related survey [42], RL is considered as a method for session-based RSs [46, 51, 52].”
Deep Learning Based Recommender System (2017, background): “Sequential Information w/t User ID [16, 29, 33, 35, 73, 91, 118, 134, 144, 161, 174, 176, 190, 195, 199, 206]”
RecSim: A Configurable Simulation Platform for Recommender Systems (2019, background): “Interest in the application of RL to optimizing these sequences has been rarer [Shani et al., 2005] though the recent successes of deep RL have spurred activity in the use of RL for recommendation [Gauci et al., 2018, Zheng et al., 2018, Chen et al., 2018, Zhao et al., 2018, Ie et al., 2019].”
Deep Reinforcement Learning for List-wise Recommendations (2017, background): “Recommender systems have become increasingly popular in recent years, and have been utilized in a variety of domains including movies, music, books, point of interests, and social events[5, 21, 22, 32–36].”
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (2018, background): “[33, 34] propose a novel page-wise recommendation framework based on deep reinforcement learning, which can optimize a page of”
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]”
Top-K Off-Policy Correction for a REINFORCE Recommender System (2018, background): “Unfortunately, the action space grows exponentially under this set recommendation formulation [44, 50], which is prohibitively”
Artificial intelligence in recommender systems (2020, background): “The challenge of large and dynamic actions is tackled in [149] with Actor-Critic architecture to reduce the computational complexity.”
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph (2019, background): “Recently, recommendation algorithms achieve remarkable improvements during these years with help of deep learning models [12, 14, 30, 47, 49] and the successful introducing of side-information [15, 19, 22, 23, 34].”
A Survey on Session-based Recommender Systems (2019, methodology): “For example, in some cases, the items purchased in one session may not be identical or similar, instead, they may complement each other [146], e.”