The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs
Abstract
With the recent surge of location based social networks (LBSNs), activity data of millions of users has become attainable. This data contains not only spatial and temporal stamps of user activity, but also its semantic information. LBSNs can help to understand mobile users' spatial temporal activity preference (STAP), which can enable a wide range of ubiquitous applications, such as personalized context-aware location recommendation and group-oriented advertisement. However, modeling such user-specific STAP needs to tackle high-dimensional data, i.e., user-location-time-activity quadruples, which is complicated and usually suffers from a data sparsity problem. In order to address this problem, we propose a STAP model. It first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference. In order to characterize the impact of spatial features on user activity preference, we propose the notion of personal functional region and related parameters to model and infer user spatial activity preference. In order to model the user temporal activity preference with sparse user activity data in LBSNs, we propose to exploit the temporal activity similarity among different users and apply nonnegative tensor factorization to collaboratively infer temporal activity preference. Finally, we put forward a context-aware fusion framework to combine the spatial and temporal activity preference models for preference inference. We evaluate our proposed approach on three real-world datasets collected from New York and Tokyo, and show that our STAP model consistently outperforms the baseline approaches in various settings.
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Cites
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 13,868
A Multilinear Singular Value Decomposition
De Lathauwer, L., De Moor, B., & Vandewalle, J. (2000) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/s0895479896305696 · Cited by 4,106
Some Mathematical Notes on Three-Mode Factor Analysis
Tucker, L. R. (1966) · Psychometrika · doi:10.1007/bf02289464 · Cited by 3,492
Friendship and mobility: user movement in location-based social networks
Cho, E., Myers, S. A., & Leskovec, J. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020579 · Cited by 2,903
Discovering regions of different functions in a city using human mobility and POIs
Yuan, J., Zheng, Y., & Xie, X. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339561 · Cited by 1,099
Karatzoglou, A., Amatriain, X., Baltrunas, L., & Oliver, N. (2010) · Proceedings of the fourth ACM conference on Recommender systems · doi:10.1145/1864708.1864727 · Cited by 721
Exploring Millions of Footprints in Location Sharing Services
Cheng, Z., Caverlee, J., Lee, K., & Sui, D. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14109 · Cited by 684
Location-based and preference-aware recommendation using sparse geo-social networking data
Bao, J., Zheng, Y., & Mokbel, M. F. (2012) · Proceedings of the 20th International Conference on Advances in Geographic Information Systems · doi:10.1145/2424321.2424348 · Cited by 671
Collaborative location and activity recommendations with GPS history data
Zheng, V. W., Zheng, Y., Xie, X., & Yang, Q. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772795 · Cited by 661
Self-citationAn Empirical Study of Geographic User Activity Patterns in Foursquare
Noulas, A., Scellato, S., Mascolo, C., & Pontil, M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14175 · Cited by 490
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Yang, D., Zhang, D., & Qu, B. (2016) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2814575 · Cited by 308
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Metadata
- Authors
- Yang, D., Zhang, D., Zheng, V. W., & Yu, Z.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2015-01
- Volume
- 45
- Issue
- 1
- Pages
- 129-142
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2071702404
- Cited by
- 846
- References
- 26
- FWCI
- 41.50
- Type
- journal article
- Language
- en
- Open access
- closed
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