User-Specific Rating Prediction for Mobile Applications via Weight-based Matrix Factorization

With the dramatic growth of mobile application (app) markets, users can find various apps with any functionalities they desire in these markets. However, the huge amounts of apps make it quite a challenge for users to discover good apps efficiently. Previous studies recommend apps by considering all apps equal without capturing the specific interests of each individual user. To address this problem, we propose a model called Weight-based Matrix Factorization (WMF), which can capture user-specific interests and give a more accurate prediction on these apps. WMF views each user as a document and each app as a word, and calculates the weight of each app for target users. The weights are calculated by employing term frequency inverse document frequency (TF-IDF) algorithm, which are then introduced into matrix factorization to predict app ratings. Comprehensive experiments are conducted on a real-world datasets with 5057 users and 4496 apps. The experimental results show that WMF achieves a convincing performance and surpasses other existing prediction models.