A Group Discovery Method Based on Collaborative Filtering and Knowledge Graph for IoT Scenarios

Abstract

With the massive growth of Internet-of-Things (IoT) devices, how to provide users with recommendation services in the IoT environment has become a research hotspot. Group discovery, as a prerequisite step of group recommendation that can be used to assist groups of users to select services in IoT-enriched environments, has an important impact on recommendation performance. However, existing group recommendation solutions assume that a user belongs to a specific group and ignore the possible correlation between the user's preferences and other groups' preferences. In addition, existing solutions treat group members as equal individuals and assign them equal weights, which makes it hard to meet the user's accurate recommendation requirements. Furthermore, these methods focus on group members' explicit preference information while ignoring implicit preferences. To address these problems, we propose a group discovery method based on collaborative filtering and knowledge graph (GD-CFKG). This method first uses the attention mechanism to learn the embedding of service entities from knowledge graphs and interaction between users and services to achieve users' own preferences embedding. Considering that the preferences of similar users will help to attain accurate target user's preferences, we then train users' final preferences embedding by collaborative filtering and word2vec method. We conduct experiments to evaluate our approach using the MovieLens and Douban data sets. Experimental results show that our proposed method has better group recommendation performance than those baseline methods.