Dynamic graph construction for improving diversity of recommendation

Abstract

The diversity of recommendation has attracted a lot of attention in recommender systems due to its ability to improve user experience. Most of the diversified recommendation tasks usually exploit user-item interaction records for mining user explicit preferences, while rarely explore the user-item non-interaction records. For diversified recommendations, however, the neglected non-interaction records are especially important for capturing users' potential interests to improve the diversity of recommendation. Moreover, the majority of diversified recommendation methods run in two stages: first optimizing the users and items embeddings by relevance, then generating the diversified items list by post-processing methods. These methods are not end-to-end thus can hardly reach global optimum. To solve above limitations, we propose an end-to-end Dynamic Diversified Graph framework (DDGraph) which constructs the user-item graph dynamically based on the users and items embeddings. Technically, we initialize a user-item interaction graph and dynamically update the graph by selecting a set of diverse items for each user and building links between the items and user. The selection of diverse items can be achieved by different candidate selection operators. Specifically, we design a Quantile Progressive Candidate Selection (QPCS) operator based on the latent space division. To the best of our knowledge, our method is the first to diversify recommendation results by dynamic end-to-end graph construction and the QPCS has a higher computational efficiency than other operators. Extensive experiments on the benchmark dataset illustrate the effectiveness and superiority of the DDGraph framework.