Deep Reinforcement Learning for List-wise Recommendations

Code

• github.com/massquantity/DBRL
  pytorch★ 154
• github.com/xuyuandong/simple-ddpg
  tf★ 0
• github.com/paige-chang/Music-Recommendation-System
  tf★ 0
• github.com/egipcy/LIRD
  none★ 0
• github.com/luozachary/drl-rec
  none★ 0
• github.com/tuantran23012000/Recommendation-system
  pytorch★ 0
• github.com/UnibucProjects/DeepRLRecommenderSystem
  none★ 0

Abstract

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedbacks. In particular, we introduce an online user-agent interacting environment simulator, which can pre-train and evaluate model parameters offline before applying the model online. Moreover, we validate the importance of list-wise recommendations during the interactions between users and agent, and develop a novel approach to incorporate them into the proposed framework LIRD for list-wide recommendations. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.

Tasks

Reproductions