Identifying functional aspects from user reviews for functionality†based mobile app recommendation

The explosive growth of mobile apps makes it difficult for users to find their needed apps in a crowded market. An effective mechanism that provides high quality app recommendations becomes necessary. However, existing recommendation techniques tend to recommend similar items but fail to consider users’ functional requirements, making them not effective in the app domain. In this article, we propose a recommendation architecture that can generate app recommendations at the functionality level. We address the redundant recommendation problem in the app domain by highlighting users’ functional requirements, an element that has received scant attention from existing recommendation research. Another main feature of our work is extracting app functionalities from textural user reviews for recommendation. We also propose an effective approach for functionality extraction. Experiments conducted on a real†world dataset show that our proposed AppRank method outperforms other commonly used recommendation methods. In particular, it doubles the recall value of the second best method under an extremely sparse setting, increases the overall ranking accuracy of the second best method by 14.27%, and retains a high diversity of 0.99.