Mobile Game Recommendation Based on Clustering Reliable Reviews

Mobile games are a popular form of entertainment that attract many users, but it is a difficult problem to find the right game from a large number of games. Mobile game community platforms such as TapTap provide player ratings and reviews functions to help users understand the quality and features of games, but there are a lot of fake reviews among them, which may lead to users making wrong choices. In this paper, we propose a Clustering-based Reliable Review Recommender Model (CRRRM) for this problem. The model comprehensively uses the text, the attributes of reviewers and reviews, filters out reliable reviews from unlabeled data through fuzzy C-means algorithm, and then makes recommendations. We collect more than 400 thousand mobile game reviews data on the TapTap platform for experiments. The results show that our model significantly outperforms the state-of-the-art baseline model in recommendation performance.