Personalized Mobile Information Recommendation Based on Fine-Grained User Behaviors

Abstract

At present, the enthusiasm of users to score actively in mobile information recommendation system is generally poor. Moreover, the existing research works rarely start with the analysis of fine-grained reading behaviors of mobile terminal users, but mostly based on the analysis of reading content and the improvement of model. It is difficult to find out the objective, short-term and local behavioral preferences of users. To solve the above problems, we propose six kinds of explicit fine-grained reading behaviors and integrate them into the user reading interest model to form the SVR-ALL model. The effectiveness of these six fine-grained behaviors is verified by ablative experiments. On the basis of SVR-ALL model, four implicit fine-grained reading behaviors are further mined by considering the difference of user reading habits, and then propose the user reading preference model called F-AFC. The updating mechanism for user preference designed in F-AFC can fully reflect the changes of users’ reading habits in different periods. Experiments show that the accuracy of the user interest model considering user’s reading preference and its update can be improved to some extent.