Abstract
Micro-blogging services and location-based social networks, such as Twitter, Weibo, and Foursquare, enable users to post short messages with timestamps and geographical annotations. The rich spatial-temporal-semantic information of individuals embedded in these geo-annotated short messages provides exciting opportunity to develop many context-aware applications in ubiquitous computing environments. Example applications include contextual recommendation and contextual search. To obtain accurate recommendations and most relevant search results, it is important to capture users’ contextual information (e.g., time and location) and to understand users’ topical interests and intentions. While time and location can be readily captured by smartphones, understanding user’s interests and intentions calls for effective methods in modeling user mobility behavior. Here, user mobility refers to who visits which place at what time for what activity. That is, user mobility behavior modeling must consider user (Who), spatial (Where), temporal (When), and activity (What) aspects. Unfortunately, no previous studies on user mobility behavior modeling have considered all of the four aspects jointly, which have complex interdependencies. In our preliminary study, we propose the first solution named W4 (short for Who, Where, When, and What) to discover user mobility behavior from the four aspects. In this article, we further enhance W4 and propose a nonparametric Bayesian model named EW4 (short for Enhanced W4). EW4 requires no parameter tuning and achieves better results over W4 in our experiments. Given some of the four aspects of a user (e.g., time), our model is able to infer information of the other aspects (e.g., location and topical words). Thus, our model has a variety of context-aware applications, particularly in contextual search and recommendation. Experimental results on two real-world datasets show that the proposed model is effective in discovering users’ spatial-temporal topics. The model also significantly outperforms state-of-the-art baselines for various tasks including location prediction for tweets and requirement-aware location recommendation.
- Amr Ahmed, Liangjie Hong, and Alexander J. Smola. 2013. Hierarchical geographical modeling of user locations from social media posts. In 22nd International World Wide Web Conference (WWW’13). 25--36. http://dl.acm.org/citation.cfm?id=2488392. Google ScholarDigital Library
- Sandro Bauer, Anastasios Noulas, Diarmuid Ó Séaghdha, Stephen Clark, and Cecilia Mascolo. 2012. Talking places: Modelling and analysing linguistic content in foursquare. In 2012 International Conference on Privacy, Security, Risk and Trust (PASSAT’12) and 2012 International Conference on Social Computing (SocialCom’12). 348--357. DOI:http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.107 Google ScholarDigital Library
- Paul N. Bennett, Filip Radlinski, Ryen W. White, and Emine Yilmaz. 2011. Inferring and using location metadata to personalize web search. In Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’11). 135--144. DOI:http://dx.doi.org/10.1145/2009916.2009938 Google ScholarDigital Library
- D. Brockmann, L. Hufnagel, and T. Geisel. 2006. The scaling laws of human travel. Nature 439, 7075 (2006), 462--465.Google ScholarCross Ref
- Zhiyuan Cheng, James Caverlee, Kyumin Lee, and Daniel Z. Sui. 2011. Exploring millions of footprints in location sharing services. In Proceedings of the 5th International Conference on Weblogs and Social Media. Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2783.Google Scholar
- Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility: User movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1082--1090. DOI:http://dx.doi.org/10.1145/2020408.2020579 Google ScholarDigital Library
- Bin Cui, Hong Mei, and Beng Chin Ooi. 2014. Big data: The driver for innovation in databases. National Science Review 1, 1 (2014), 27--30.Google ScholarCross Ref
- Jacob Eisenstein, Brendan O’Connor, Noah A. Smith, and Eric P. Xing. 2010. A latent variable model for geographic lexical variation. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP’10). 1277--1287. Google ScholarDigital Library
- Marta C. Gonzalez, Cesar A. Hidalgo, and Albert-Laszlo Barabasi. 2008. Understanding individual human mobility patterns. Nature 453, 7196 (2008), 779--782.Google Scholar
- Qiang Hao, Rui Cai, Changhu Wang, Rong Xiao, Jiang-Ming Yang, Yanwei Pang, and Lei Zhang. 2010. Equip tourists with knowledge mined from travelogues. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). 401--410. DOI:http://dx.doi.org/10.1145/1772690.1772732 Google ScholarDigital Library
- Gregor Heinrich. 2011. Infinite LDA implementing the HDP with minimum code complexity. Technical note, Feb 170 (2011).Google Scholar
- Thomas Hofmann. 1999. Probabilistic latent semantic indexing. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99). 50--57. DOI:http://dx.doi.org/10.1145/312624.312649 Google ScholarDigital Library
- Liangjie Hong, Amr Ahmed, Siva Gurumurthy, Alexander J. Smola, and Kostas Tsioutsiouliklis. 2012. Discovering geographical topics in the twitter stream. In Proceedings of the 21st World Wide Web Conference 2012 (WWW’12). 769--778. DOI:http://dx.doi.org/10.1145/2187836.2187940 Google ScholarDigital Library
- Bo Hu and Martin Ester. 2013. Spatial topic modeling in online social media for location recommendation. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). 25--32. DOI:http://dx.doi.org/10.1145/2507157.2507174 Google ScholarDigital Library
- Rosie Jones, Ahmed Hassan Awadallah, and Fernando Diaz. 2008. Geographic features in web search retrieval. In Proceedings of the 5th ACM Workshop on Geographic Information Retrieval (GIR’08). 57--58. DOI:http://dx.doi.org/10.1145/1460007.1460023 Google ScholarDigital Library
- Sheila Kinsella, Vanessa Murdock, and Neil O’Hare. 2011. “I’m eating a sandwich in Glasgow”: Modeling locations with tweets. In Proceedings of the 3rd International CIKM Workshop on Search and Mining User-Generated Contents (SMUC’11). 61--68. DOI:http://dx.doi.org/10.1145/2065023.2065039 Google ScholarDigital Library
- Christoph Carl Kling, Jérôme Kunegis, Sergej Sizov, and Steffen Staab. 2014. Detecting non-gaussian geographical topics in tagged photo collections. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM’14). 603--612. DOI:http://dx.doi.org/10.1145/2556195.2556218 Google ScholarDigital Library
- Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. IEEE Computer 42, 8 (2009), 30--37. DOI:http://dx.doi.org/10.1109/MC.2009.263 Google ScholarDigital Library
- Chenliang Li, Aixin Sun, and Anwitaman Datta. 2012. Twevent: Segment-based event detection from tweets. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM’12). 155--164. DOI:http://dx.doi.org/10.1145/2396761.2396785 Google ScholarDigital Library
- Wen Li, Pavel Serdyukov, Arjen P. de Vries, Carsten Eickhoff, and Martha Larson. 2011. The where in the tweet. In Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM’11). 2473--2476. DOI:http://dx.doi.org/10.1145/2063576.2063995 Google ScholarDigital Library
- Qiaozhu Mei, Chao Liu, Hang Su, and ChengXiang Zhai. 2006. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In Proceedings of the 15th international conference on World Wide Web (WWW’06). 533--542. DOI:http://dx.doi.org/10.1145/1135777.1135857 Google ScholarDigital Library
- Kevin P. Murphy. 2007. Conjugate Bayesian analysis of the gaussian distribution. def 1, 2σ2 (2007), 16.Google Scholar
- Carl Edward Rasmussen. 1999. The infinite Gaussian mixture model. In Advances in Neural Information Processing Systems 12. 554--560.Google Scholar
- Alan Ritter, Mausam, Oren Etzioni, and Sam Clark. 2012. Open domain event extraction from Twitter. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). 1104--1112. DOI:http://dx.doi.org/10.1145/2339530.2339704 Google ScholarDigital Library
- Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. 2010. Earthquake shakes Twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). 851--860. DOI:http://dx.doi.org/10.1145/1772690.1772777 Google ScholarDigital Library
- Sergej Sizov. 2010. GeoFolk: Latent spatial semantics in web 2.0 social media. In Proceedings of the 3rd International Conference on Web Search and Web Data Mining (WSDM’10). 281--290. DOI:http://dx.doi.org/10.1145/1718487.1718522 Google ScholarDigital Library
- Chaoming Song, Tal Koren, Pu Wang, and Albert-László Barabási. 2010a. Modelling the scaling properties of human mobility. Nature Physics 6, 10 (2010), 818--823.Google ScholarCross Ref
- Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. 2010b. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018--1021.Google Scholar
- Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, and David M. Blei. 2006. Hierarchical Dirichlet processes. Journl of the American Statistical Association 101, 476 (2006), 1566--1581. DOI:http://dx.doi.org/10.1198/016214506000000302Google ScholarCross Ref
- Chong Wang, Jinggang Wang, Xing Xie, and Wei-Ying Ma. 2007. Mining geographic knowledge using location aware topic model. In Proceedings of the 4th ACM Workshop on Geographic Information Retrieval (GIR’07). 65--70. DOI:http://dx.doi.org/10.1145/1316948.1316967 Google ScholarDigital Library
- Benjamin Wing and Jason Baldridge. 2011. Simple supervised document geolocation with geodesic grids. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 955--964. Google ScholarDigital Library
- Jinyun Yan, Wei Chu, and Ryen W. White. 2014. Cohort modeling for enhanced personalized search. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’14). 505--514. DOI:http://dx.doi.org/10.1145/2600428.2609617 Google ScholarDigital Library
- Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’11). 325--334. DOI:http://dx.doi.org/10.1145/2009916.2009962 Google ScholarDigital Library
- Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas S. Huang. 2011. Geographical topic discovery and comparison. In Proceedings of the 20th International Conference on World Wide Web (WWW’11). 247--256. DOI:http://dx.doi.org/10.1145/1963405.1963443 Google ScholarDigital Library
- Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat-Thalmann. 2013a. Time-aware point-of-interest recommendation. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’13). 363--372. DOI:http://dx.doi.org/10.1145/2484028.2484030 Google ScholarDigital Library
- Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat-Thalmann. 2013b. Who, where, when and what: Discover spatio-temporal topics for twitter users. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13). 2013. 605--613. DOI:http://dx.doi.org/10.1145/2487575.2487576 Google ScholarDigital Library
- Quan Yuan, Gao Cong, and Aixin Sun. 2014. Graph-based point-of-interest recommendation with geographical and temporal influences. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM’14). 659--668. DOI:http://dx.doi.org/10.1145/2661829.2661983 Google ScholarDigital Library
Index Terms
- Who, Where, When, and What: A Nonparametric Bayesian Approach to Context-aware Recommendation and Search for Twitter Users
Recommendations
Who, where, when and what: discover spatio-temporal topics for twitter users
KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data miningMicro-blogging services, such as Twitter, and location-based social network applications have generated short text messages associated with geographic information, posting time, and user ids. The availability of such data received from users offers a ...
Discovering geographical topics in the twitter stream
WWW '12: Proceedings of the 21st international conference on World Wide WebMicro-blogging services have become indispensable communication tools for online users for disseminating breaking news, eyewitness accounts, individual expression, and protest groups. Recently, Twitter, along with other online social networking services ...
Characteristics of Similar-Context Trending Hashtags in Twitter: A Case Study
Web Services – ICWS 2020AbstractTwitter is a popular social networking platform that is widely used in discussing and spreading information on global events. Twitter trending hashtags have been one of the topics for researcher to study and analyze. Understanding the posting ...
Comments