Jia-Dong Zhang
Chi-Yin Chow
Skip Abstract Section
Abstract
Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which benefits users who wish to explore new places and businesses to discover potential customers. In LBSNs, social and geographical influences have been intensively used in location recommendations. However, human movement also exhibits spatiotemporal sequential patterns, but only a few current studies consider the spatiotemporal sequential influence of locations on users’ check-in behaviors. In this article, we propose a new gravity model for location recommendations, called LORE, to exploit the spatiotemporal sequential influence on location recommendations. First, LORE extracts sequential patterns from historical check-in location sequences of all users as a Location-Location Transition Graph (L2TG), and utilizes the L2TG to predict the probability of a user visiting a new location through the developed additive Markov chain that considers the effect of all visited locations in the check-in history of the user on the new location. Furthermore, LORE applies our contrived gravity model to weigh the effect of each visited location on the new location derived from the personalized attractive force (i.e., the weight) between the visited location and the new location. The gravity model effectively integrates the spatiotemporal, social, and popularity influences by estimating a power-law distribution based on (i) the spatial distance and temporal difference between two consecutive check-in locations of the same user, (ii) the check-in frequency of social friends, and (iii) the popularity of locations from all users. Finally, we conduct a comprehensive performance evaluation for LORE using three large-scale real-world datasets collected from Foursquare, Gowalla, and Brightkite. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art location recommendation techniques.
- Jie Bao, Yu Zheng, and Mohamed F. Mokbel. 2012. Location-based and preference-aware recommendation using sparse geo-social networking data. In Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 199--208. Google Scholar
Digital Library
- Zaiben Chen, Heng Tao Shen, and Xiaofang Zhou. 2011. Discovering popular routes from trajectories. In Proceedings of the 27th IEEE International Conference on Data Engineering. 900--911. Google ScholarDigital Library
- An-Jung Cheng, Yan-Ying Chen, Yen-Ta Huang, Winston H. Hsu, and Hong-Yuan Mark Liao. 2011. Personalized travel recommendation by mining people attributes from community-contributed photos. In Proceedings of the 19th ACM International Conference on Multimedia. 83--92. Google ScholarDigital Library
- Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. In Proceedings of the 26th National Conference on Artificial Intelligence. 17--23.Google Scholar
- Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2605--2611. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Gregory Ference, Mao Ye, and Wang-Chien Lee. 2013. Location recommendation for out-of-town users in location-based social networks. In Proceedings of the 22nd ACM Conference on Information and Knowledge Management. 721--726. Google ScholarDigital Library
- Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2013. Exploring temporal effects for location recommendation on location-based social networks. In Proceedings of the 7th ACM Conference on Recommender Systems. 93--100. Google ScholarDigital Library
- Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2015. Content-aware point of interest recommendation on location-based social networks. In Proceedings of the 29th National Conference on Artificial Intelligence. 1721--1727.Google Scholar
- Huiji Gao, Jiliang Tang, and Huan Liu. 2012. gSCorr: Modeling geo-social correlations for new check-ins on location-based social networks. In Proceedings of the 21st ACM Conference on Information and Knowledge Management. 1582--1586. Google ScholarDigital Library
- Marta C. González, César A. Hidalgo, and Albert-László Barabási. 2008. Understanding individual human mobility patterns. Nature 453, 7196 (2008), 779--782.Google Scholar
- Hsun-Ping Hsieh, Cheng-Te Li, and Shou-De Lin. 2014. Measuring and recommending time-sensitive routes from location-based data. ACM Transactions on Intelligent Systems and Technology 5, 3 (2014), 45:1--45:27. 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. 25--32. Google ScholarDigital Library
- Bo Hu, M. Jamali, and M. Ester. 2013. Spatio-temporal topic modeling in mobile social media for location recommendation. In Proceedings of the 13th IEEE International Conference on Data Mining. 1073--1078.Google Scholar
- Yehuda Koren. 2010. Collaborative filtering with temporal dynamics. Communications of the ACM 53, 4 (2010), 89--97. Google ScholarDigital Library
- Takeshi Kurashima, Tomoharu Iwata, Takahide Hoshide, Noriko Takaya, and Ko Fujimura. 2013. Geo topic model: Joint modeling of user’s activity area and interests for location recommendation. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. Rome, Italy, 375--384. Google ScholarDigital Library
- Takeshi Kurashima, Tomoharu Iwata, Go Irie, and Ko Fujimura. 2010. Travel route recommendation using geotags in photo sharing sites. In Proceedings of the 19th ACM Conference on Information and Knowledge Management. 579--588. Google ScholarDigital Library
- Kenneth Wai-Ting Leung, Dik Lun Lee, and Wang-Chien Lee. 2011. CLR: A collaborative location recommendation framework based on co-clustering. In Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 305--314. Google ScholarDigital Library
- Justin J. Levandoski, Mohamed Sarwat, Ahmed Eldawy, and Mohamed F. Mokbel. 2012. LARS: A location-aware recommender system. In Proceedings of the 28th IEEE International Conference on Data Engineering. 450--461. Google ScholarDigital Library
- Defu Lian, Xing Xie, Vincent W. Zheng, Nicholas Yuan, Fuzheng Zhang, and Enhong Chen. 2015. CEPR: A collaborative exploration and periodically returning model for location prediction. ACM Transactions on Intelligent Systems and Technology 6, 1 (2015), 8:1--8:27. http://dl.acm.org/citation.cfm?id=2629557. Google ScholarDigital Library
- Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. 2014. GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 831--840. Google ScholarDigital Library
- Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. 2013a. Learning geographical preferences for point-of-interest recommendation. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1043--1051. Google ScholarDigital Library
- Bin Liu and Hui Xiong. 2013. Point-of-interest recommendation in location based social networks with topic and location awareness. In Proceedings of the SIAM International Conference on Data Mining. 396--404.Google ScholarCross Ref
- Bin Liu, Hui Xiong, Spiros Papadimitriou, Yanjie Fu, and Zijun Yao. 2014c. A general geographical probabilistic factor model for point of interest recommendation. IEEE Transactions on Knowledge and Data Engineering.Google Scholar
- Xin Liu, Yong Liu, Karl Aberer, and Chunyan Miao. 2013b. Personalized point-of-interest recommendation by mining users’ preference transition (short paper). In Proceedings of the 22nd ACM Conference on Information and Knowledge Management. 733--738. Google ScholarDigital Library
- Yu Liu, Zhengwei Sui, Chaogui Kang, and Yong Gao. 2014a. Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS ONE 9, 1 (2014), e86026.Google ScholarCross Ref
- Yong Liu, Wei Wei, Aixin Sun, and Chunyan Miao. 2014b. Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM Conference on Information and Knowledge Management. 739--748. Google ScholarDigital Library
- Eric Hsueh-Chan Lu, Ching-Yu Chen, and Vincent S. Tseng. 2012. Personalized trip recommendation with multiple constraints by mining user check-in behaviors. In Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 209--218. Google ScholarDigital Library
- Yue Shi, Pavel Serdyukov, Alan Hanjalic, and Martha Larson. 2013. Nontrivial landmark recommendation using geotagged photos. ACM Transactions on Intelligent Systems and Technology 4, 3 (2013), 47:1--47:27. Google ScholarDigital Library
- Roger W. Sinnott. 1984. Virtues of the Haversine. Sky and Telescope 68, 2 (1984), 159--159.Google Scholar
- Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. 2010. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018--1021.Google Scholar
- Hao Wang, Manolis Terrovitis, and Nikos Mamoulis. 2013. Location recommendation in location-based social networks using user check-in data. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 384--393. Google ScholarDigital Library
- Xiangyu Wang, Yi-Liang Zhao, Liqiang Nie, Yue Gao, Weizhi Nie, Zheng-Jun Zha, and Tat-Seng Chua. 2015. Semantic-based location recommendation with multimodal venue semantics. IEEE Transactions on Multimedia 17, 3 (2015), 409--419.Google ScholarDigital Library
- Dingqi Yang, Daqing Zhang, Zhiyong Yu, and Zhu Wang. 2013. A sentiment-enhanced personalized location recommendation system. In Proceedings of the 24th ACM Conference on Hypertext and Social Media. 119--128. Google ScholarDigital Library
- Zijun Yao, Bin Liu, Yanjie Fu, Hui Xiong, and Yong Ge. 2014. User preference learning with multiple information fusion for restaurant recommendation. In Proceedings of the SIAM International Conference on Data Mining. 470--478.Google Scholar
- 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. 325--334. Google ScholarDigital Library
- Hongzhi Yin, Bin Cui, Yizhou Sun, Zhiting Hu, and Ling Chen. 2014. LCARS: A spatial item recommender system. ACM Transactions on Information Systems 32, 3 (2014), 11:1--11:37. Google ScholarDigital Library
- Hongzhi Yin, Yizhou Sun, Bin Cui, Zhiting Hu, and Ling Chen. 2013. LCARS: A location-content-aware recommender system. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 221--229. Google ScholarDigital Library
- Zhijun Yin, Liangliang Cao, Jiawei Han, Jiebo Luo, and Thomas S. Huang. 2011. Diversified trajectory pattern ranking in geo-tagged social media. In Proceedings of the SIAM International Conference on Data Mining. 980--991.Google Scholar
- Josh Jia-Ching Ying, Wen-Ning Kuo, Vincent S. Tseng, and Eric Hsueh-Chan Lu. 2014. Mining user check-in behavior with a random walk for urban point-of-interest recommendations. ACM Transactions on Intelligent Systems and Technology 5, 3 (2014), 40:1--40:26. Google ScholarDigital Library
- Josh Jia-Ching Ying, Wang-Chien Lee, and Vincent S. Tseng. 2013. Mining geographic-temporal-semantic patterns in trajectories for location prediction. ACM Transactions on Intelligent Systems and Technology 5, 1 (2013), 2:1--2:33. Google ScholarDigital Library
- Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wen-Ning Kuo, and Vincent S. Tseng. 2012. Urban point-of-interest recommendation by mining user check-in behaviors. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing. 63--70. Google ScholarDigital Library
- Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat-Thalmann. 2013. Time-aware point-of-interest recommendation. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. 363--372. 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 Conference on Information and Knowledge Management. 659--668. Google ScholarDigital Library
- Quan Yuan, Gao Cong, Kaiqi Zhao, Zongyang Ma, and Aixin Sun. 2015. Who, where, when and what: A non-parametric Bayesian approach to context-aware recommendation and search for Twitter users. ACM Transactions on Information Systems 33, 1 (2015), 2:1--2:33. http://dl.acm.org/citation.cfm?id=2699667. Google ScholarDigital Library
- Jia-Dong Zhang and Chi-Yin Chow. 2013. iGSLR: Personalized geo-social location recommendation - a kernel density estimation approach. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 344--353. Google ScholarDigital Library
- Jia-Dong Zhang and Chi-Yin Chow. 2015a. CoRe: Exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations. Information Sciences 293 (2015), 163--181.Google ScholarCross Ref
- Jia-Dong Zhang and Chi-Yin Chow. 2015b. GeoSoCa: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. Google ScholarDigital Library
- Jia-Dong Zhang and Chi-Yin Chow. 2015c. TICRec: A probabilistic framework to utilize temporal influence correlations for time-aware location recommendations. IEEE Transactions on Services Computing, Still in press: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7061519.Google Scholar
- Jia-Dong Zhang, Chi-Yin Chow, and Yanhua Li. 2014a. iGeoRec: A personalized and efficient geographical location recommendation framework. IEEE Transactions on Services Computing, Still in press: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6824843.Google Scholar
- Jia-Dong Zhang, Chi-Yin Chow, and Yanhua Li. 2014b. LORE: Exploiting sequential influence for location recommendations. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 103--112. Google ScholarDigital Library
- Jia-Dong Zhang, Gabriel Ghinita, and Chi-Yin Chow. 2014c. Differentially private location recommendations in geosocial networks. In Proceedings of the 15th IEEE International Conference on Mobile Data Management. 59--68. Google ScholarDigital Library
- Yi-Liang Zhao, Liqiang Nie, Xiangyu Wang, and Tat-Seng Chua. 2014. Personalized recommendations of locally interesting venues to tourists via cross-Region community matching. ACM Transactions on Intelligent Systems and Technology 5, 3 (2014), 50:1--50:26. Google ScholarDigital Library
- Vincent W. Zheng, Yu Zheng, Xing Xie, and Qiang Yang. 2012. Towards mobile intelligence: Learning from GPS history data for collaborative recommendation. Artificial Intelligence 184--185 (2012), 17--37. Google ScholarDigital Library
- Yu Zheng, Lizhu Zhang, Zhengxin Ma, Xing Xie, and Wei-Ying Ma. 2011. Recommending friends and locations based on individual location history. ACM Transactions on the Web 5, 1 (2011), 5:1--5:44. Google ScholarDigital Library
- Yan-Tao Zheng, Zheng-Jun Zha, and Tat-Seng Chua. 2012. Mining travel patterns from geotagged photos. ACM Transactions on Intelligent Systems and Technology 3, 3 (2012), 56:1--56:18. Google ScholarDigital Library
Index Terms
- Spatiotemporal Sequential Influence Modeling for Location Recommendations: A Gravity-based Approach
Recommendations
LORE: exploiting sequential influence for location recommendations
SIGSPATIAL '14: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information SystemsProviding 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 ...
iGSLR: personalized geo-social location recommendation: a kernel density estimation approach
SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information SystemsWith the rapidly growing location-based social networks (LBSNs), personalized geo-social recommendation becomes an important feature for LBSNs. Personalized geo-social recommendation not only helps users explore new places but also makes LBSNs more ...
A Location Recommendation Model Based on User Behavior and Sequence Influence
Internet of Things – ICIOT 2023AbstractThere are evident sequential patterns in human visits to different locations in daily life, which has led to the emergence of numerous sequence-based geographic location recommendation methods. However, previous research has mainly focused on ...
Comments