Mobile Application Rating Prediction via Feature-Oriented Matrix Factorization

With the proliferation of mobile application (app) markets (e.g., Google Play, Apple App Store), predicting user preferences on apps becomes a challenging problem. Different from previous work, we assume that a user likes an app because he/she likes certain features of the app (e.g., permission, genre, topic). Based on this assumption, we propose a feature-oriented approach to predict user preferences on apps. Specifically, we transform the original app rating matrix to feature rating data and predict the unknown ratings on the features through a latent factor model, instead of directly predicting ratings on apps. The predicted user ratings on features can be used to generate the ratings on apps. Two integration methods are presented to give different significance for feature preferences. The approach has some obvious advantages: as it integrates feature information to analyze the details of user preference, it can generalize better as the feature rating data is denser, and improve the interpretation of the prediction of app ratings. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed approach.