A DQN-based Recommender System for Item-list Recommendation

Recommender systems are widely used to help users locate information by suggesting goods that best match users' needs and preferences. Most existing recommender systems are built by implementing supervised learning-based algorithms. Unfortunately, the large search space with lots of local optimums makes it challenging for the existing recommender systems to apply in more and more new upcoming recommendation scenarios, such as the novel item-list recommendation scenario proposed by FUXI AI Lab, Netease. It requires to recommend an item list in each step, where the item list is one of the thousands of combinations of items. To tackle these challenges, recent researchers resort to adopting reinforcement learning (RL) for recommendations. It is natural to formalize item recommendation problem as a multi-step decision-making problem and use exploration and exploitation of RL to address the problem of large search space. In this paper, we propose a DQN-based recommender system for item-list recommendation that achieves second place in track II of the RL-based RecSys and it only performs slightly worse than the first place.