Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference
Abstract
Recent years have witnessed a rapid adoption of mobile devices and a dramatic proliferation of mobile applications (Apps for brevity). However, the large number of mobile Apps makes it difficult for users to locate relevant Apps. Therefore, recommending Apps becomes an urgent task. Traditional recommendation approaches focus on learning the interest of a user and the functionality of an item (e.g., an App) from a set of user-item ratings, and they recommend an item to a user if the item's functionality well matches the user's interest. However, Apps could have privileges to access a user's sensitive resources ( e.g., contact, message, and location). As a result, a user chooses an App not only because of its functionality, but also because it respects the user's privacy preference. To the best of our knowledge, this paper presents the first systematic study on incorporating both interest-functionality interactions and users' privacy preferences to perform personalized App recommendations. Specifically, we first construct a new model to capture the trade-off between functionality and user privacy preference. Then we crawled a real-world dataset (16,344 users, 6,157 Apps, and 263,054 ratings) from Google Play and use it to comprehensively evaluate our model and previous methods. We find that our method consistently and substantially outperforms the state-of-the-art approaches, which implies the importance of user privacy preference on personalized App recommendations. Moreover,…
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Liu et al. (2015), Gu et al. (2022), and Shahidi et al. (2022) produced evidence in favor of the privacy calculus theory by demonstrating that whether a user chooses to use an app is a result of the trade-off between the app's functionality or usefulness and the user's privacy concerns.
“Liu et al. (2015), Gu et al. (2022), and Shahidi et al. (2022), for example, produced evidence in favor of the privacy calculus theory by demonstrating that whether a user chooses to use an app is a result of the trade-off between the app's functionality (or usefulness) and the user's privacy concerns.”
From The privacy and control paradoxes in the context of smartphone apps (2022) · cites this paper as Liu et al. (2015)
The results from Study 1 suggest that the decision to install an app (and grant data access permissions as required) results from a trade-off between the app's functionality importance and the user's privacy concerns.
“The results from Study 1 suggest that the decision to install an app (and grant data access permissions as required) results from a trade-off between the app's functionality importance and the user's privacy concerns (e.g., Dinev and Hart, 2006;Liu et al., 2015;Shahidi et al., 2022).”
From The privacy and control paradoxes in the context of smartphone apps (2022) · cites this paper as Liu et al., 2015
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Scrutinizing Mobile App Recommendation: Identifying Important App-Related Indicators
Lin, J., Sugiyama, K., Kan, M., & Chua, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48051-0_15 · Cited by 3 · see citing works
Understanding the Long-term Dynamics of Mobile App Usage Context via Graph Embedding
Fan, Y., Tu, Z., Li, T., Cao, H., Xia, T., Li, Y., Chen, X., & Zhang, L. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3110141 · Cited by 3 · see citing works
Mobile App Risk Ranking via Exclusive Sparse Coding
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A Deep Dive into the Featured iOS Apps
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Evaluation of Mobile Applications to Advance Router Configuration Skills
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An Approach for Privacy-Aware Mobile App Package Recommendation
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- Authors
- Liu, B., Kong, D., Cen, L., Gong, N. Z., Jin, H., & Xiong, H.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Samsung Research America, San Jose, CA, USA
- Purdue University West Lafayette (US)
- University of California, Berkeley (US)
- Venue
- Proceedings of the Eighth ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2015-02-02
- Pages
- 315-324
- OpenAlex
- W2017281925
- Cited by
- 98
- References
- 29
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 3 · 2022: 5 · 2021: 11 · 2020: 11 · 2019: 17 · 2018: 14 · 2017: 15 · 2016: 13 · 2015: 3
- FWCI
- 13.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- mobile app recommendation, personalized recommendation, privacy preferences, user privacy, sensitive data access, Google Play Store, user ratings
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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