Rating Prediction for Mobile Applications via Collective Matrix Factorization Considering App Categories

Abstract

The rapid development of Internet and mobile communication, smart phone, tablet computer and other mobile devices become widespread, they have become an indispensable part of life, changing the human lifestyle, way of working and learning style, it brings great convenience and fun to us. This paper mainly discusses the status of personalized recommendation system, mobile application and analyse the main problems of recommendation system. This research proposes a rising mobile application domain, which uses web crawling technique to get explicit feedback from a real-world mobile application market. The explicit feedback is the users’ ratings data on Apps and the corresponding category information. To establish a mobile application recommendation model based on user interest and categorization information and propose a novel collective matrix factorization algorithm. Through the crawled data, the improved algorithm, and the traditional collaborative. The traditional mobile application recommendation systems are mostly based on keyword search, popularity, download usage and categories, thus they do not make personalized recommendation to users, and invalid to find applications that users are interested in. Through the user’s historical behaviour, a personalized recommendation system can be established to recommend the applications that fit user’s interests effectively. This paper is based on the collective matrix factorization using user interest such as user’s ratings and category information, designs a personalized recommendation system for mobile applications by using the relationship between them.