A Novel Learning Approach to Improve Mobile Application Recommendation Diversity

With the popularity of smart phones, plenty of mobile phone applications are developed to meet people's various needs, and mobile application recommendation has become a popular and challenging topic. Most studies focus on learning user preferences from various information both on user-side and APP-side, and recommending based on user similarity or app similarity. However, these methods all have a high probability to cause serious homogenization problems that can not meet users' unknown/new needs. Therefore, recommending diverse apps is more likely to cover users' all preferences, and even guide users to discover new needs and interests. To this end, we give the definition of Application Diversity that taking into account the similarity between apps and the relevance of categories, and propose a novel application recommendation approach that consists of two parts, P-Stair Neural Network (P-SNN) and Dynamic Adjustment Method (DAM). First, P-SNN learns user preferences from multi-dimensional data by using deep neural networks techniques, and predicts users' ratings for uninstalled applications. Then, DAM selects TOP-N applications as the final recommend list with considering both user preferences and recommend diversity. Several experiments on different datasets shows that our algorithm effectively improves the diversity of recommendations in the case of similar accuracy.