The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Mining Mobile User Preferences for Personalized Context-Aware Recommendation
Abstract
Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.
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“These multiple sensors can capture historical data that is related to the usage records of users (Zhu et al., 2012).”
From SoCoMo marketing for travel and tourism: Empowering co-creation of value (2015) · cites this paper as Zhu et al., 2012
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Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583
“, smartphone datasets such as phone call logs [84, 101], SMS Log [29], mobile application usages logs [137] [117], mobile phone notification logs [73] etc.”
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570
SoCoMo marketing for travel and tourism: Empowering co-creation of value
Buhalis, D., & Foerste, M. (2015) · Journal of Destination Marketing & Management · doi:10.1016/j.jdmm.2015.04.001 · Cited by 545
A Survey of App Store Analysis for Software Engineering
Martin, W., Sarro, F., Jia, Y., Zhang, Y., & Harman, M. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2630689 · Cited by 459
Creating value from Social Big Data: Implications for Smart Tourism Destinations
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Metadata
- Authors
- Zhu, H., Chen, E., Xiong, H., Yu, K., Cao, H., & Tian, J.
- Affiliations
- University of Science and Technology of China (CN)
- Rutgers, The State University of New Jersey (US)
- Nokia (China) (CN)
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2014-12-15
- Volume
- 5
- Issue
- 4
- Pages
- 1-27
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W2018936209
- Cited by
- 157
- References
- 32
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 9 · 2022: 5 · 2021: 16 · 2020: 11 · 2019: 23 · 2018: 28 · 2017: 22 · 2016: 21 · 2015: 9 · 2014: 1
- FWCI
- 48.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- context-aware recommendation, personalized recommendation systems, preference distribution
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