Article

Content-aware point of interest recommendation on location-based social networks

Authors:

Huan LiuAuthors Info & Claims

AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence

Pages 1721 - 1727

Published: 25 January 2015 Publication History

Abstract

The rapid urban expansion has greatly extended the physical boundary of users' living area and developed a large number of POIs (points of interest). POI recommendation is a task that facilitates users' urban exploration and helps them filter uninteresting POIs for decision making. While existing work of POI recommendation on location-based social networks (LBSNs) discovers the spatial, temporal, and social patterns of user check-in behavior, the use of content information has not been systematically studied. The various types of content information available on LBSNs could be related to different aspects of a user's check-in action, providing a unique opportunity for POI recommendation. In this work, we study the content information on LB-SNs w.r.t. POI properties, user interests, and sentiment indications. We model the three types of information under a unified POI recommendation framework with the consideration of their relationship to check-in actions. The experimental results exhibit the significance of content information in explaining user behavior, and demonstrate its power to improve POI recommendation performance on LBSNs.

References

[1]

Bensoussan, B. E., and Fleisher, C. S. 2012. Analysis Without Paralysis: 12 Tools to Make Better Strategic Decisions. FT Press.

[2]

Chang, J., and Sun, E. 2011. Location 3: How users share and respond to location-based data on social networking sites. Proceedings of the ICWSM.

[3]

Cheng, Z.; Caverlee, J.; Lee, K.; and Sui, D. 2011. Exploring millions of footprints in location sharing services. In Proceedings of the ICWSM.

[4]

Cheng, C.; Yang, H.; King, I.; and Lyu, M. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. AAAI, Canada.

[5]

Cheng, C.; Yang, H.; Lyu, M. R.; and King, I. 2013. Where you like to go next: Successive point-of-interest recommendation. In Proceedings of the IJCAI, 2605-2611.

[6]

Cho, E.; Myers, S.; and Leskovec, J. 2011. Friendship and mobility: user movement in location-based social networks. In Proceedings of the SIGKDD, 1082-1090.

[7]

Dingqi Yang, Daqing Zhang, Z. Y., and Wang, Z. 2013. A sentiment-enhanced personalized location recommendation system. ACM Hypertext.

[8]

Gao, H., and Liu, H. 2014. Data analysis on location-based social networks. In Mobile Social Networking. Springer. 165-194.

[9]

Gao, H.; Tang, J.; Hu, X.; and Liu, H. 2013. Exploring temporal effects for location recommendation on location-based social networks. In Proceedings of the RecSys, 93-100.

[10]

Gao, H.; Tang, J.; and Liu, H. 2012. Exploring social-historical ties on location-based social networks. In Proceedings of the ICWSM.

[11]

Hu, B., and Ester, M. 2013. Spatial topic modeling in online social media for location recommendation. In Proceedings of the RecSys, 25-32.

[12]

Jorge Nocedal, S. W. 1999. Numerial Optimization. Springer.

[13]

Koren, Y. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceeding of the SIGKDD, 426-434.

[14]

Lee, D.; Seung, H.; et al. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788-791.

[15]

Li, Y.; Hu, J.; Zhai, C.; and Chen, Y. 2010. Improving one-class collaborative filtering by incorporating rich user information. In Proceedings of the CIKM, 959-968. ACM.

[16]

Liu, B., and Xiong, H. 2013. Point-of-interest recommendation in location based social networks with topic and location awareness. Proc. of SDM 396-404.

[17]

Liu, B.; Fu, Y.; Yao, Z.; and Xiong, H. 2013. Learning geographical preferences for point-of-interest recommendation. In Proceedings of the SIGKDD, 1043-1051. ACM.

[18]

Liu, J.; Ji, S.; and Ye, J. 2009. Multi-task feature learning via efficient l 2, 1-norm minimization. In Proceedings of the UAI, 339-348. AUAI Press.

[19]

Long, X., and Joshi, J. 2013. A hits-based poi recommendation algorithm for location-based social networks. In Proceedings of the ASONAM, 642-647. ACM.

[20]

Ma, H.; Yang, H.; Lyu, M.; and King, I. 2008. Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the CIKM, 931-940. ACM.

[21]

Noulas, A.; Scellato, S.; Mascolo, C.; and Pontil, M. 2011. An empirical study of geographic user activity patterns in foursquare. Proceeding of the ICWSM.

[22]

Paatero, P., and Tapper, U. 1994. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics (2).

[23]

Pan, R., and Scholz, M. 2009. Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering. In Proceedings of the SIGKDD, 667-676. ACM.

[24]

Pan, R.; Zhou, Y.; Cao, B.; Liu, N. N.; Lukose, R.; Scholz, M.; and Yang, Q. 2008. One-class collaborative filtering. In Proceeding of the ICDM, 502-511. IEEE.

[25]

Salakhutdinov, R., and Mnih, A. 2007. Probabilistic matrix factorization. In NIPS, volume 1, 2-1.

[26]

Scellato, S.; Noulas, A.; Lambiotte, R.; and Mascolo, C. 2011. Socio-spatial properties of online location-based social networks. Proceeding of the ICWSM 11.

[27]

Sridharan, S. 2001. Usability and Reliability of the User Action Framework: A Theoretical Foundation for Usability Engineering Activities. Ph.D. Dissertation, Citeseer.

[28]

Tang, J.; Hu, X.; Gao, H.; and Liu, H. 2013. Exploiting local and global social context for recommendation. In Proceedings of the IJCAI, 2712-2718. AAAI Press.

[29]

Ye, M.; Yin, P.; Lee, W.; and Lee, D. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceeding of the SIGIR, 325-334.

[30]

Ye, M.; Liu, X.; and Lee, W. 2012. Exploring social influence for recommendation - a probabilistic generative approach. In Proceeding of the SIGIR, 325-334.

[31]

Ye, M.; Yin, P.; and Lee, W. 2010. Location recommendation for location-based social networks. In Proceedings of the SIGSPATIAL, 458-461.

[32]

Yin, H.; Sun, Y.; Cui, B.; Hu, Z.; and Chen, L. 2013. Lcars: a location-content-aware recommender system. In Proceedings of the SIGKDD, 221-229. ACM.

[33]

Yuan, Q.; Cong, G.; Ma, Z.; Sun, A.; and Thalmann, N. M. 2013. Time-aware point-of-interest recommendation. In Proceedings of the SIGIR, 363-372. ACM.

[34]

Zhang, C.; Shou, L.; Chen, K.; Chen, G.; and Bei, Y. 2012. Evaluating geo-social influence in location-based social networks. In Proceedings of the CIKM, 1442-1451. ACM.

[35]

Zheng, Y., and Xie, X. 2011. Learning travel recommendations from user-generated gps traces. TIST 2(1):2.

[36]

Zheng, Y.; Zhang, L.; Xie, X.; and Ma, W. 2009. Mining interesting locations and travel sequences from gps trajectories. In WWW, 791-800. ACM.

[37]

Zhou, D.; Wang, B.; Rahimi, S.; and Wang, X. 2012. A study of recommending locations on location-based social network by collaborative filtering. Proceeding of the AAI.

Cited By

Zhang YWang ZYu WHu LJiang PGai KChen X(2024)Soft Contrastive Sequential RecommendationACM Transactions on Information Systems10.1145/366532542:6(1-28)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3665325
Zhu GCao JChen LWang YBu ZYang SWu JWang Z(2023)A Multi-Task Graph Neural Network with Variational Graph Auto-Encoders for Session-Based Travel Packages RecommendationACM Transactions on the Web10.1145/357703217:3(1-30)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1145/3577032
Qin YWang YSun FJu WHou XWang ZCheng JLei JZhang MChua TLauw HSi LTerzi ETsaparas P(2023)DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest RecommendationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570408(508-516)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570408
Show More Cited By

Recommendations

Time-aware point-of-interest recommendation
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to ...
A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable ...
Evaluation of Social, Geography, Location Effects for Point-of-Interest Recommendation
ICDMW '13: Proceedings of the 2013 IEEE 13th International Conference on Data Mining Workshops

Recently, location-based social network services have become very popular. Therefore, point of interests (POIs) recommendation has also become a promising and hot research problem. In POIs recommendation, the number of locations could be more than the ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence

January 2015

4331 pages

ISBN:0262511290

Sponsors

Association for the Advancement of Artificial Intelligence

Publisher

AAAI Press

Publication History

Published: 25 January 2015

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

54
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang YWang ZYu WHu LJiang PGai KChen X(2024)Soft Contrastive Sequential RecommendationACM Transactions on Information Systems10.1145/366532542:6(1-28)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3665325
Zhu GCao JChen LWang YBu ZYang SWu JWang Z(2023)A Multi-Task Graph Neural Network with Variational Graph Auto-Encoders for Session-Based Travel Packages RecommendationACM Transactions on the Web10.1145/357703217:3(1-30)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1145/3577032
Qin YWang YSun FJu WHou XWang ZCheng JLei JZhang MChua TLauw HSi LTerzi ETsaparas P(2023)DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest RecommendationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570408(508-516)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570408
Feng JLi YLin ZRong CSun FGuo DJin D(2021)Context-aware Spatial-Temporal Neural Network for Citywide Crowd Flow Prediction via Modeling Long-range Spatial DependencyACM Transactions on Knowledge Discovery from Data10.1145/347757716:3(1-21)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.1145/3477577
Yu FCui LGuo WLu XLi QLu H(2020)A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in DataProceedings of The Web Conference 202010.1145/3366423.3380202(1264-1274)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380202
Han JYamana H(2019)Geographic Diversification of Recommended POIs in Frequently Visited AreasACM Transactions on Information Systems10.1145/336250538:1(1-39)Online publication date: 17-Oct-2019
https://dl.acm.org/doi/10.1145/3362505
Isaj SPedersen T(2019)Seed-Driven Geo-Social Data ExtractionProceedings of the 16th International Symposium on Spatial and Temporal Databases10.1145/3340964.3340973(11-20)Online publication date: 19-Aug-2019
https://dl.acm.org/doi/10.1145/3340964.3340973
Gao QZhou FTrajcevski GZhang KZhong TZhang F(2019)Predicting Human Mobility via Variational AttentionThe World Wide Web Conference10.1145/3308558.3313610(2750-2756)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313610
Qian TLiu BNguyen QYin H(2019)Spatiotemporal Representation Learning for Translation-Based POI RecommendationACM Transactions on Information Systems10.1145/329549937:2(1-24)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1145/3295499
Li RJiang JJu CWang WCulpepper JMoffat ABennett PLerman K(2019)CORALSProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3290995(69-77)Online publication date: 30-Jan-2019
https://dl.acm.org/doi/10.1145/3289600.3290995
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents