Wei Ou
ABSTRACT
A very difficult aspect of machine learning in location-based social networks is extracting proper features that can reflect some characteristics of users and locations from the very constrained datasets. In this paper we propose a new statistical model to extract features for users that reflect users' interests solely from users' connections to each other. Also based on the interest features of users we propose to extract a demographic feature for each location that reflects the demographic characteristics of visitors to the location. To test its effectiveness, we incorporate the features into existing location recommendation systems and semantic location annotation algorithms. The experiment result shows satisfactory improvements that verify the meaningfulness of the features.
- Flake, G.W., Lawrence, S. and Giles, C.L. 2000. Efficient identification of web communities. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining(2000), 150--160. Google Scholar
Digital Library
- Shi, J. and Malik, J. 2000. Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 22, 8 (2000), 888--905. Google ScholarDigital Library
- Girvan, M. and Newman, M.E. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences. 99, 12 (2002), 7821--7826.Google ScholarCross Ref
- Tyler, J.R., Wilkinson, D.M. and Huberman, B.A. 2005. E-mail as spectroscopy: Automated discovery of community structure within organizations. The Information Society. 21, 2 (2005), 143--153.Google ScholarCross Ref
- Newman, M.E. and Girvan, M. 2004. Finding and evaluating community structure in networks. Physical review E. 69, 2 (2004), 026113.Google Scholar
- Ramaswamy, L., Deepak, P., Polavarapu, R., Gunasekera, K., Garg, D., Visweswariah, K. and Kalyanaraman, S. 2009. Caesar: A context-aware, social recommender system for low-end mobile devices. Mobile Data Management: Systems, Services and Middleware, 2009. MDM'09. Tenth International Conference on (2009), 338--347. Google ScholarDigital Library
- Zheng, Y. and Xie, X. 2010. Learning location correlation from gps trajectories. Mobile Data Management (MDM), 2010 Eleventh International Conference on (2010), 27--32. Google ScholarDigital Library
- Bao, J., Zheng, Y. and Mokbel, M.F. 2012. Location-based and preference-aware recommendation using sparse geo-social networking data. Proceedings of the 20th International Conference on Advances in Geographic Information Systems (2012), 199--208. Google ScholarDigital Library
- Chow, C.-Y., Bao, J. and Mokbel, M.F. 2010. Towards location-based social networking services. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks (2010), 31--38. Google ScholarDigital Library
- Ye, M., Yin, P., Lee, W.-C. and Lee, D.-L. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (2011), 325--334. Google ScholarDigital Library
- Ye, M., Shou, D., Lee, W.-C., Yin, P. and Janowicz, K. 2011. On the semantic annotation of places in location-based social networks. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (2011), 520--528. Google ScholarDigital Library
- Lian, D. and Xie, X. 2011. Learning location naming from user check-in histories. Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (2011), 112--121. Google ScholarDigital Library
- Chon, Y., Lane, N.D., Li, F., Cha, H. and Zhao, F. 2012. Automatically characterizing places with opportunistic crowdsensing using smartphones. Proceedings of the 2012 ACM Conference on Ubiquitous Computing (2012), 481--490. Google ScholarDigital Library
- Blei, D.M., Ng, A.Y. and Jordan, M.I. 2003. Latent dirichlet allocation. the Journal of machine Learning research. 3, (2003), 993--1022. Google ScholarDigital Library
- Heinrich, G. 2005. Parameter estimation for text analysis. Technical report.Google Scholar
- Hastings, M.B. 2006. Community detection as an inference problem. Physical Review E. 74, 3 (2006), 035102.Google ScholarCross Ref
- Leicht, E.A. and Newman, M.E. 2008. Community structure in directed networks. Physical review letters. 100, 11 (2008), 118703.Google Scholar
- Kemp, C., Griffiths, T.L. and Tenenbaum, J.B. 2004. Discovering latent classes in relational data. (2004).Google Scholar
- Zhang, H., Qiu, B., Giles, C.L., Foley, H.C. and Yen, J. 2007. An LDA-based community structure discovery approach for large-scale social networks. Intelligence and Security Informatics, 2007 IEEE (2007), 200--207.Google ScholarCross Ref
- Zhang, H., Giles, C.L., Foley, H.C. and Yen, J. 2007. Probabilistic community discovery using hierarchical latent gaussian mixture model. AAAI (2007), 663--668. Google ScholarDigital Library
- Henderson, K. and Eliassi-Rad, T. 2009. Applying latent dirichlet allocation to group discovery in large graphs. Proceedings of the 2009 ACM symposium on Applied Computing (2009), 1456--1461. Google ScholarDigital Library
- Bao, J., Zheng, Y., Wilkie, D. and Mokbel, M.F. 2014. A survey on recommendations in location-based social networks. Submitted to GeoInformatica. (2014).Google Scholar
Index Terms
- Extracting User Interests from Graph Connections for Machine Learning in Location-Based Social Networks
Recommendations
Location recommendation for out-of-town users in location-based social networks
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge ManagementMost previous research on location recommendation services in location-based social networks (LBSNs) makes recommendations without considering where the targeted user is currently located. Such services may recommend a place near her hometown even if ...
A HITS-based POI recommendation algorithm for location-based social networks
ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningLocation-Based Social Networks (LBSNs), (also called as Geo-Social Networks), has been attracting more and more users by providing services that integrate social activities with location information. LBSN systems usually provide support for indicating ...
Location-Specific Influence Quantification in Location-Based Social Networks
Survey Paper, Research Commentary and Regular PapersLocation-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other’s physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or ...
Reviews
CK Raju
Social networks contain information regarding connections amongst members, responses of members to various posts, profile information, and members' preferences for certain geographical sites or locations. Analysis of social networks is widely perceived to be helpful in revealing communities and thereby behavioral patterns. Could there be a correlation between preferences shown by members to certain geographical sites and some of the other common features of a social network's members__?__ What role do demographic features play in determining location-based preferences of members__?__ The author uses two datasets to derive the features. The first one pertains to users and their check-in records in London, and the second one pertains to users and their check-in records in Berlin. A friendship model is proposed where a connected graph is visualized with nodes forming the users in the social network, the edges revealing a common topic, and the weight of the edges revealing the extent of participation of the connected users. A multi-label support vector machine (SVM) classifier is then trained with a set of features that include user interests and demographic features derived from the friendship model. Ninety percent of the dataset is used for training the SVM classifier, while the model is tested on the remaining ten percent. Precision and recall figures are analyzed for both the datasets to highlight the utility of prediction, even for imbalanced datasets. The influence on demographic features for communities in different locations is also analyzed by training the classifiers on datasets separately, with and without those features. This work is likely to generate interest in agencies associated with deploying social networks, as it can suggest targeted advertising to location-based service providers like restaurants, transportation providers, educational institutions, and so on, by recommending prospective users as targets. Online Computing Reviews Service
Access critical reviews of Computing literature here
Become a reviewer for Computing Reviews.
Comments