A topic attention mechanism and factorization machines based mobile application recommendation method

Faced with the explosive growth of mobile applications, how to recommend mobile applications accurately and efficiently for users to choose their desirable and interesting mobile applications, which has become a challenging issue nowadays. To solve this problem, we propose a topic attention mechanism and FMs based mobile application recommendation method. Firstly, it uses LSA to obtain the global topic of mobile application description text. Then, the local semantic representations of mobile application are trained by BiLSTM model. Secondly, as for the global topic information and local semantic information in the content representation of mobile application description text, attention mechanism is performed to distinguish the contribution degree of different words and gain their weight values. Thirdly, the classification and prediction of mobile application are completed by using the softmax activation function through a full connection layer. Finally, based on user’s searching requirement, it exploits factorization machines to combine the various features of the classified mobile applications to rank and recommend the user’s expected mobile application with higher predicted score. The evaluation is conducted on a real and open dataset Mobile App Store, and the experimental results indicate that the performance of the proposed approach is better than other baseline methods in terms of precision, recall, F1-score, MAE, RMSE, and AUC.