IIDQN: An Incentive Improved DQN Algorithm in EBSN Recommender System

Abstract

Event-based Social Networks (EBSN), combining online networks with offline users, provide versatile event recommendations for offline users through complex social networks. However, there are some issues that need to be solved in EBSN: (1) The online static data could not satisfy the online dynamic recommendation demand; (2) the implicit behavior information tends to be ignored, reducing the accuracy of the recommendation algorithm; and (3) the online recommendation description is inconsistent with the offline activity. To address these issues, an Incentive Improved DQN (IIDQN) based on Deep Q-Learning Networks (DQN) is proposed. More specifically, we introduce the agents to interact with the environment through online dynamic data. Furthermore, we consider two types of implicit behavior information: the length of the user’s browsing time and the user’s implicit behavior factors. As for the problem of inconsistency, based on blockchain technology, a new activities event approach on EBSN is proposed, where all activities are recorded on the chain. Finally, the simulation results indicate that the IIDQN algorithm greatly outperforms in mean rewards and recommendation performance than before DQN.