||Recently, the explosive development of mobile device has dramatically changed human life, mobile application becomes pervasive as well. Nowadays, there have released about 3 million mobile applications. Due to the tremendous and still increasing number of mobile application, user get harder to find needed apps. |
To tackle this problem, we propose a personalized recommender system based on the features of textual data. Specifically, we apply LDA to extract hidden topics of user reviews and use the probability of topic distribution to represent the features of app. Further, construct user profile based on his or her consumed apps in order to realize user requirements and preference. Eventually, for each app, we take account both the topic distribution and user preference to estimate a recommended score for target user and sort candidate apps by descending score to come out a personalized app recommended list.
For the evaluation, we crawl the real-world dataset and adopt Recall@M as the measurement of performance. The experimental result shows our proposed mechanism outperforms the baseline and is able to enhance the performance of the state-of-the-art recommender systems. We then concluded that the user feedback is an effective variable to represent the features of app and plays significant role on app personalized recommender system.