@inproceedings{4c8542f542fc4787a97418ecce25dcbc,
title = "Exploiting user actions for app recommendations",
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.",
author = "Kai Shu and Suhang Wang and Huan Liu and Jiliang Tang and Yi Chang and Ping Luo",
year = "2018",
month = oct,
day = "24",
doi = "10.1109/ASONAM.2018.8508447",
language = "English (US)",
series = "Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "139--142",
editor = "Andrea Tagarelli and Chandan Reddy and Ulrik Brandes",
booktitle = "Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018",
address = "United States",
note = "10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 ; Conference date: 28-08-2018 Through 31-08-2018",
}