Combining collaborative filtering and topic modeling for more accurate android mobile app library recommendation

Huan Yu, Xin Xia, Xiaoqiong Zhao, Weiwei Qiu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

Abstract

The applying of third party libraries is an integral part of many mobile applications. With the rapid development of mobile technologies, there are many free third party libraries for developers to download and use. However, there are a large number of third party libraries which always iterate rapidly, it is hard for developers to find available libraries within them. Several previous studies have proposed approaches to recommend third party libraries, which works in the scenario where a developer knows some required libraries, and needs to find other relevant libraries with limited knowledge. In the paper, to further improve the performance of app library recommendation, we propose an approach which combines collaborative filtering and topic modeling techniques. In the collaborative filtering component, given a new app, our approach recommends libraries by using its similar apps. In the topic modelling component, our approach first extracts the topics from the textual description of mobile apps, and given a new app, our approach recommends libraries based on the libraries used by the apps which has similar topic distributions.We perform experiments on a set of 1,013 apps, and the results show that our approach improves the state-of-the-art by a substantial margin.

Original languageEnglish
Title of host publicationInternetware 2017 - 9th Asia-Pacific Symposium on Internetware
Subtitle of host publicationSeptember 23, 2017, Shanghai China
EditorsHong Mei, Jian Lyu, Zhi Jin, Wenyun Zhao
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
VolumeF130951
ISBN (Electronic)1595930361, 9781450353137
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventAsia-Pacific Symposium on Internetware 2017 - Shanghai, China
Duration: 23 Sep 201723 Sep 2017
Conference number: 9th
http://www.se.fudan.edu.cn/events/Internetware2017/

Conference

ConferenceAsia-Pacific Symposium on Internetware 2017
Abbreviated titleInternetware 2017
CountryChina
CityShanghai
Period23/09/1723/09/17
Internet address

Keywords

  • Android App
  • Collaborative Filtering
  • Library Recommendation
  • Topic Modeling

Cite this

Yu, H., Xia, X., Zhao, X., & Qiu, W. (2017). Combining collaborative filtering and topic modeling for more accurate android mobile app library recommendation. In H. Mei, J. Lyu, Z. Jin, & W. Zhao (Eds.), Internetware 2017 - 9th Asia-Pacific Symposium on Internetware: September 23, 2017, Shanghai China (Vol. F130951). [17] Association for Computing Machinery (ACM). https://doi.org/10.1145/3131704.3131721