Popularity Sensitive Topic Recommendation
Everyday lots of apps are released for various purposes, app recommendation is becoming increasingly important.However most existing recommender systems make recommendations for app users rather than app developers.We observed that the description of an app plays a very important role when a user decided to buy and use an app, especially for those who are unfamiliar with the app.Therefore, we decided to make recommendations for app developers, helping them write an intriguing description.We present a novel model that recommends the topics related to app popularity and their probabilities for developers, which we name Popularity-Sensitive Topic Recommendation (PSTR).Our method can be used to look for topics highly related to app popularity, and generate optimized probability distribution of topics which enable apps to obtain higher popularity score.What is novel about our method is that recommender system should not only consider regular users, but app developers.
Popularity sensitive Recommender system Mobile apps Developers
Li Wan Xiaocan Yang
Computer Science and Technology College Chongqing University, China
国际会议
三亚
英文
214-222
2015-12-26（万方平台首次上网日期，不代表论文的发表时间）