Abstract
Mobile Applications (or Apps) are becoming more and more popular in recent years, which has attracted increasing attention on mobile App recommendations. The majority of existing App recommendation algorithms focus on mining App functionality or user usage data for discovering user preferences; while actions taken by a user when he/she decides to download an App or not are ignored. In realistic scenarios, a user will first view the description of the App and then decide if he/she wants to download it or not. The actions such as viewing or downloading provide rich information about users' preferences and tastes for Apps, which have great potentials to advance App recommendations. However, the work on exploring action data for App recommendations is rather limited. Therefore, in this paper we study the novel problem of exploiting user actions for App recommendations. We propose a new framework ActionRank, which simultaneously captures various signals from user actions for App recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 |
| Editors | Andrea Tagarelli, Chandan Reddy, Ulrik Brandes |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 139-142 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538660515 |
| DOIs | |
| State | Published - Oct 24 2018 |
| Event | 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain Duration: Aug 28 2018 → Aug 31 2018 |
Publication series
| Name | Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 |
|---|
Conference
| Conference | 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 |
|---|---|
| Country | Spain |
| City | Barcelona |
| Period | 8/28/18 → 8/31/18 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Sociology and Political Science
- Communication
- Computer Networks and Communications
- Information Systems and Management
Cite this
}
Exploiting user actions for app recommendations. / Shu, Kai; Wang, Suhang; Liu, Huan; Tang, Jiliang; Chang, Yi; Luo, Ping.
Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. ed. / Andrea Tagarelli; Chandan Reddy; Ulrik Brandes. Institute of Electrical and Electronics Engineers Inc., 2018. p. 139-142 8508447 (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Exploiting user actions for app recommendations
AU - Shu, Kai
AU - Wang, Suhang
AU - Liu, Huan
AU - Tang, Jiliang
AU - Chang, Yi
AU - Luo, Ping
PY - 2018/10/24
Y1 - 2018/10/24
N2 - Mobile Applications (or Apps) are becoming more and more popular in recent years, which has attracted increasing attention on mobile App recommendations. The majority of existing App recommendation algorithms focus on mining App functionality or user usage data for discovering user preferences; while actions taken by a user when he/she decides to download an App or not are ignored. In realistic scenarios, a user will first view the description of the App and then decide if he/she wants to download it or not. The actions such as viewing or downloading provide rich information about users' preferences and tastes for Apps, which have great potentials to advance App recommendations. However, the work on exploring action data for App recommendations is rather limited. Therefore, in this paper we study the novel problem of exploiting user actions for App recommendations. We propose a new framework ActionRank, which simultaneously captures various signals from user actions for App recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.
AB - Mobile Applications (or Apps) are becoming more and more popular in recent years, which has attracted increasing attention on mobile App recommendations. The majority of existing App recommendation algorithms focus on mining App functionality or user usage data for discovering user preferences; while actions taken by a user when he/she decides to download an App or not are ignored. In realistic scenarios, a user will first view the description of the App and then decide if he/she wants to download it or not. The actions such as viewing or downloading provide rich information about users' preferences and tastes for Apps, which have great potentials to advance App recommendations. However, the work on exploring action data for App recommendations is rather limited. Therefore, in this paper we study the novel problem of exploiting user actions for App recommendations. We propose a new framework ActionRank, which simultaneously captures various signals from user actions for App recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.
UR - http://www.scopus.com/inward/record.url?scp=85057314206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057314206&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2018.8508447
DO - 10.1109/ASONAM.2018.8508447
M3 - Conference contribution
T3 - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
SP - 139
EP - 142
BT - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
A2 - Tagarelli, Andrea
A2 - Reddy, Chandan
A2 - Brandes, Ulrik
PB - Institute of Electrical and Electronics Engineers Inc.
ER -