CFDIL: a context-aware feature deep interaction learning for app recommendation

The rapid development of mobile Internet has spawned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical usage data to explore users' preferences and then make recommendations. Although traditional methods have achieved certain success, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users' preferences, which is often overlooked by traditional methods. To overcome the aforementioned problems, we proposed a context-aware feature deep interaction learning (CFDIL) method to explore users' preferences and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users' preferences modeling by constructing novel user and app feature portraits. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which shows that CFDIL outperforms the benchmark methods.