Functional feature-aware APP recommendation with personalised PageRank

With the explosive growth of mobile applications and the widespread deployment of internet of things (IoT) services, it has become an urgent problem to recommend applications for users that they are interested in. And most of the current mobile APP recommendation methods are based on the user's behaviour data or context-aware information, to some extent, ignoring the user's preference for APP functional features. Therefore, this paper proposes a functional feature-aware mobile APP recommendation method, named S-AppRank. S-AppRank first extracts the functional features of mobile applications and their internal association through weight calculation, then constructs a directed graph of user functional features with the association rules, and then adds user ratings to the traditional PageRank algorithm, incorporating explicit feedback into recommendation personally. Finally, the user's interests in the overall APP are predicted, and a recommendation list is generated. The experiments on the real dataset of Huawei application market show that the S-AppRank proposed in this paper is better than other comparison methods.