Abstract Recent years have witnessed the ever-growing popularity of location-based social network (LBSN) services. Top-N place recommendation, which aims at retrieving N most appealing places for a target user, has thus gained increasing importance. Yet existing solutions to this problem either provide non-personalized recommendations by selecting nearby popular places, or resort to collaborative filtering (CF) by treating each place as an independent item, overlooking the geographical and semantic correlations among places. In this paper, we propose GoTo, a collaborative recommender that provides top-N personalized place recommendation in LBSNs. Compared with existing methods, GoTo achieves its effectiveness by exploiting the wisdom of the so-called local experts, namely those who are geographically close and have similar preferences with regard to a certain user. At the core of GoTo lies a novel user similarity measure called geo-topical similarity, which combines geographical and semantic correlations among places for discovering local experts. In specific, the geo-topical similarity uses Gaussian mixtures to model users' real-life geographical patterns, and extracts users' topical preferences from the attached tags of historically visited places. Extensive experiments on real LBSN datasets show that compared with baseline methods, GoTo can improve the performance of top-N place recommendation by up to 50% in terms of accuracy.
This work is supported by the National Natural Science Foundation of China under Grant No. M1552002 and the National High Technology Research and Development Program of China under Grant No. 2014AA015205.
Corresponding Authors: Tian-Lei Hu
About author: Rong Wang is a Ph.D. candidate in the Database Laboratory of College of Computer Science and Technology, Zhejiang University, Hangzhou. His research interests include data mining and information retrieval.
Cite this article:
Rong Wang, Tian-Lei Hu, Gang Chen.Where Do Local Experts Go? Evaluating User Geo-Topical Similarity for Top-N Place Recommendation[J] Journal of Computer Science and Technology, 2018,V33(1): 190-206
 González M C, Hidalgo C A, Barabási A L. Understanding individual human mobility patterns. Nature, 2008, 453(7196):779-782. Ye M, Yin P F, Lee W C. Location recommendation for location-based social networks. In Proc. the 18th SIGSPATIAL Int. Conf. Advances in Geographic Information Systems, November 2010, pp.458-461. Ye M, Yin P F, Lee W C, Lee D L. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proc. the 34th Int. ACM SIGIR Conf. Research and Development in Information Retrieval, July 2011, pp.325-334. Horozov T, Narasimhan N, Vasudevan V. Using location for personalized POI recommendations in mobile environments. In Proc. Int. Symp Applications and the Internet, Jan. 2006, pp.124-129. Levandoski J J, Sarwat M, Eldawy A, Mokbel M F. LARS:A location-aware recommender system. In Proc. the 28th Int. Conf. Data Engineering, April 2012, pp.450-461. McPherson M, Smith-Lovin L, Cook J M. Birds of a feather:Homophily in social networks. Annual Review of Sociology, 2001, 27:415-444. Ferman A M, Errico J H, van Beek P, Sezan M I. Contentbased filtering and personalization using structured metadata. In Proc. the 2nd ACM/IEEE-CS Joint Conf. Digital Libraries, July 2002. Melville P, Mooney R J, Nagarajan R. Content-boosted collaborative filtering for improved recommendations. In Proc. the 18th National Conf. Artificial Intelligence, July 28-August 1, 2002, pp.187-192. Lops P, De Gemmis M, Semeraro G. Content-based recommender systems:State of the art and trends. In Recommender Systems Handbook, Ricci F, Rokach L, Shapira B, Kantor P B (eds.), Springer, 2011, pp.73-105. Breese J S, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. the 14th Conf. Uncertainty in Artificial Intelligence, July 1998, pp.43-52. Herlocker J L, Konstan J A, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering. In Proc. the 22nd Annual Int. ACM SIGIR Conf. Research and Development in Information Retrieval, August 1999, pp.230-237. Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th Int. Conf. World Wide Web, May 2001, pp.285-295. George T, Merugu S. A scalable collaborative filtering framework based on co-clustering. In Proc. the 5th IEEE Int. Conf. Data Mining, November 2005 pp.625-628. Billsus D, Pazzani M J. Learning collaborative information filters. In Proc. the 15th Int. Conf. Machine Learning, July 1998, pp.46-54. Heckerman D, Chickering D M, Meek C, Rounthwaite R, Kadie C. Dependency networks for inference, collaborative filtering, and data visualization. Journal of Machine Learning Research, 2001, 1:49-75. Ma H, King I, Lyu M R. Learning to recommend with social trust ensemble. In Proc. the 32nd Int. ACM SIGIR Conf. Research and Development in Information Retrieval, July 2009, pp.203-210. Konstas I, Stathopoulos V, Jose J M. On social networks and collaborative recommendation. In Proc. the 32nd Int. ACM SIGIR Conf. Research and Development in Information Retrieval, July 2009, pp.195-202. Guy I, Zwerdling N, Ronen I, Carmel D, Uziel E. Social media recommendation based on people and tags. In Proc. the 33rd Int. ACM SIGIR Conf. Research and Development in Information Retrieval, July 2010, pp.194-201. Ma H, Zhou D Y, Liu C, Lyu M R, King I. Recommender systems with social regularization. In Proc. the 4th Int. Conf. Web Search and Data Mining, February 2011, pp.287-296. Zheng Y, Zhang L Z, Xie X, Ma W Y. Mining interesting locations and travel sequences from GPS trajectories. In Proc. the 18th Int. Conf. World Wide Web, April 2009, pp.791-800. Zheng V W, Zheng Y, Xie X, Yang Q. Collaborative location and activity recommendations with GPS history data. In Proc. the 19th Int. Conf. World Wide Web, April 2010, pp.1029-1038. Cheng C, Yang H Q, King I, Lyu M R. Fused matrix factorization with geographical and social influence in locationbased social networks. In Proc. the 26th AAAI Conf. Artificial Intelligence, July 2012, pp.17-23. Liu B, Fu Y J, Yao Z J, Xiong H. Learning geographical preferences for point-of-interest recommendation. In Proc. the 19th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, August 2013, pp.1043-1051. Jia Y T, Wang Y Z, Jin X L, Cheng X Q. Location prediction:A temporal-spatial Bayesian model. ACM Transactions on Intelligent Systems and Technology, 2016, 7(3):Article No. 31. Qian X M, Feng H, Zhao G S, Mei T. Personalized recommendation combining user interest and social circle. IEEE Trans. Knowledge and Data Engineering, 2014, 26(7):1763-1777. Wang X Y, Zhao Y L, Nie L Q, Gao Y, Nie W Z, Zha Z J, Chua T S. Semantic-based location recommendation with multimodal venue semantics. IEEE Trans. Multimedia, 2015, 17(3):409-419. Jiang S H, Qian X M, Shen J L, Fu Y, Mei T. Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Trans. Multimedia, 2015, 17(6):907-918. Xie M, Yin H Z, Wang H, Xu F J, Chen W T, Wang S. Learning graph-based POI embedding for location-based recommendation. In Proc. the 25th ACM Int. Conf. Information and Knowledge Management, October 2016, pp.15-24. Lei X J, Qian X M, Zhao G S. Rating prediction based on social sentiment from textual reviews. IEEE Trans. Multimedia, 2016, 18(9):1910-1921. Zhao G S, Qian X M, Kang C. Service rating prediction by exploring social mobile users' geographical locations. IEEE Trans. Big Data, 2017, 3(1):67-78. Hu L K, Sun A X, Liu Y. Your neighbors affect your ratings:On geographical neighborhood influence to rating prediction. In Proc. the 37th Int. ACM SIGIR Conf. Research and Development in Information Retrieval, July 2014, pp.345-354. Zhao G S, Qian X M, Xie X. User-service rating prediction by exploring social users' rating behaviors. IEEE Trans. Multimedia, 2016, 18(3):496-506. Jiang S, Qian X, Mei T et al. Personalized travel sequence recommendation on multi-source big social media. IEEE Trans. Big Data, 2016, 2(1):43-56. Yin Z J, Cao L L, Han J W, Zhai C X, Huang T. Geographical topic discovery and comparison. In Proc. the 20th Int. Conf. World Wide Web, March 28-April 1, 2011, pp.247-256. Zhang C, Zhang K Y, Yuan Q, Peng H R, Zheng Y, Hanratty T, Wang S W, Han J W. Regions, periods, activities:Uncovering urban dynamics via cross-modal representation learning. In Proc. the 26th Int. Conf. World Wide Web, April 2017, pp.361-370. Kang W, Tung A K H, Chen W, Li X Y, Song Q Y, Zhang C, Zhao F, Zhou X J. Trendspedia:An internet observatory for analyzing and visualizing the evolving web. In Proc. the 30th Int. Conf. Data Engineering, April 2014, pp.1206-1209. Zhang C, Zhou G Y, Yuan Q, Zhuang H L, Zheng Y, Kaplan L, Wang S W, Han J W. GeoBurst:Real-time local event detection in geo-tagged tweet streams. In Proc. the 39th Int. ACM SIGIR Conf. Research and Development in Information Retrieval, July 2016, pp.513-522. Feng W, Zhang C, Zhang W, Han J W, Wang J Y, Aggarwal C, Huang J B. STREAMCUBE:Hierarchical spatiotemporal hashtag clustering for event exploration over the twitter stream. In Proc. the 31st Int. Conf. Data Engineering, April 2015, pp.1561-1572. Abdelhaq H, Sengstock C, Gertz M. EvenTweet:Online localized event detection from twitter. Proceedings of the VLDB Endowment, 2013, 6(12):1326-1329. Yuan Q, Zhang W, Zhang C, Geng X H, Cong G, Han J. PRED:Periodic region detection for mobility modeling of social media users. In Proc. the 10th ACM Int. Conf. Web Search and Data Mining, February 2017, pp.263-272. Zhang C, Han J W, Shou L D, Lu J J, La Porta T. Splitter:Mining fine-grained sequential patterns in semantic trajectories. Proceedings of the VLDB Endowment, 2014, 7(9):769-780. Zhang C, Zhang K Y, Yuan Q, Zhang L M, Hanratty T, Han J W. GMove:Group-level mobility modeling using geo-tagged social media. In Proc. the 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, August 2016, pp.1305-1314. Cremonesi P, Koren Y, Turrin R. Performance of recommender algorithms on top-n recommendation tasks. In Proc. the 4th ACM Conf. Recommender Systems, September 2010, pp.39-46. Cho E, Myers S A, Leskovec J. Friendship and mobility:User movement in location-based social networks. In Proc. the 17th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, August 2011, pp.1082-1090. Hershey J R, Olsen P A. Approximating the Kullback Leibler divergence between Gaussian mixture models. In Proc. the IEEE Int. Conf. Acoustics Speech and Signal Processing, April 2007, pp.317-320. Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3:993-1022. Griffiths T L, Steyvers M. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(S1):5228-5235.
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