The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
LORE: exploiting sequential influence for location recommendations
Abstract
Providing location recommendations becomes an important feature for location-based social networks (LBSNs), since it helps users explore new places and makes LBSNs more prevalent to users. In LBSNs, geographical influence and social influence have been intensively used in location recommendations based on the facts that geographical proximity of locations significantly affects users' check-in behaviors and social friends often have common interests. Although human movement exhibits sequential patterns, most current studies on location recommendations do not consider any sequential influence of locations on users' check-in behaviors. In this paper, we propose a new approach called LORE to exploit sequential influence on location recommendations. First, LORE incrementally mines sequential patterns from location sequences and represents the sequential patterns as a dynamic Location-Location Transition Graph (L2TG). LORE then predicts the probability of a user visiting a location by Additive Markov Chain (AMC) with L2TG. Finally, LORE fuses sequential influence with geographical influence and social influence into a unified recommendation framework; in particular the geographical influence is modeled as two-dimensional check-in probability distributions rather than one-dimensional distance probability distributions in existing works. We conduct a comprehensive performance evaluation for LORE using two large-scale real data sets collected from Foursquare and Gowalla. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art recommendation techniques.
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Cites
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,796
Density Estimation for Statistics and Data Analysis
(1986) · Springer eBooks · doi:10.1007/978-1-4899-3324-9 · Cited by 8,525
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,059
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
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
Exploiting geographical influence for collaborative point-of-interest recommendation
Ye, M., Yin, P., Lee, W.-C., & Lee, D.-L. (2011) · Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval · doi:10.1145/2009916.2009962 · Cited by 1,111
Time-aware point-of-interest recommendation
Yuan, Q., Cong, G., Ma, Z., Sun, A., & Thalmann, N. M. (2013) · Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/2484028.2484030 · Cited by 754
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
Computing with Spatial Trajectories
(2011) · Springer eBooks · doi:10.1007/978-1-4614-1629-6 · Cited by 618
Where you like to go next: successive point-of-interest recommendation
Chen, C., Yang, H., Lyu, M. R.-T., & King, I. (2013) · International Joint Conference on Artificial Intelligence · Cited by 529
Cited by
Learning Graph-based POI Embedding for Location-based Recommendation
Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., & Wang, S. (2016) · Proceedings of the 25th ACM International on Conference on Information and Knowledge Management · doi:10.1145/2983323.2983711 · Cited by 391
Zhang, J.-D., & Chow, C.-Y. (2015) · Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/2766462.2767711 · Cited by 288
Self-citationYang, C., Bai, L., Zhang, C., Yuan, Q., & Han, J. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098094 · Cited by 286
What Your Next Check-in Might Look Like: Next Check-in Behavior Prediction
Sun, H., Cao, C., Chu, X., Hu, T., Lu, J., He, L., Wang, Z., He, H., & Xiong, H. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3625234 · Cited by 10
Recommending Reforming Trip to a Group of Users
Abbas, R., Amran, G. A., Alsanad, A., Ma, S., Almisned, F. A., Huang, J., Al Bakhrani, A. A., Ahmed, A. B., & Alzahrani, A. I. (2022) · Electronics · doi:10.3390/electronics11071037 · Cited by 5
Metadata
- Authors
- Zhang, J.-D., Chow, C.-Y., & Li, Y.
- Venue
- Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
- Publisher
- ACM
- Published
- 2014-11-04
- Pages
- 103-112
- OpenAlex
- W2074194940
- Cited by
- 254
- References
- 32
- FWCI
- 31.94
- Type
- conference paper
- Language
- en
- Open access
- closed
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