February 2018, 1(1): 11-48. doi: 10.3934/mfc.2018002

Exploring timeliness for accurate recommendation in location-based social networks

1. 

Gianforte School of Computing, Montana State University, Bozeman, MT 59717, USA

2. 

Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA

3. 

School of Computer Science and Technology, Harbin Institute of Technology, Weihai, Shandong 264209, China

Received  September 2017 Revised  November 2017 Published  February 2018

An individual's location history in the real world implies his or her interests and behaviors. This paper analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.

Citation: Yi Xu, Qing Yang, Dianhui Chu. Exploring timeliness for accurate recommendation in location-based social networks. Mathematical Foundations of Computing, 2018, 1 (1) : 11-48. doi: 10.3934/mfc.2018002
References:
[1]

G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Trans Knowl Data Eng, 734-749.

[2]

Y. AraseX. XieM. DuanT. Hara and S. Nishio, A game based approach to assign geographical relevance to web images, Proceedings of the 18th International Conference on World Wide Web, (2009), 811-820. doi: 10.1145/1526709.1526818.

[3]

Y. AraseX. XieT. Hara and S. Nishio, Mining people's trips from large scale geo-tagged photos, Proceedings of the International Conference on Multimedia, (2010), 133-142. doi: 10.1145/1873951.1873971.

[4]

M. Attarl, M. Manguoglu, I. Toroslu H, P. Symeonidis, P. Senkul and Y. Manolopoulos, Geoactivity recommendations by using improved feature combination, Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 996-1003.

[5]

L. Backstrom and J. Leskovec, Supervised random walks: Predicting and recommending links in social networks, ACM, (2011), 635-644. doi: 10.1145/1935826.1935914.

[6]

J. BaoY. Zheng and M. Mokbel, Location-based and preference-aware recommendation using sparse geo-social networking data, SIGSPATIAL '12 Proceedings of the 20th International Conference on Advances in Geographic Information Systems, (2012), 199-208. doi: 10.1145/2424321.2424348.

[7]

B. Berjani and T. Strufe, A recommendation system for spots in location-based online social networks, Proceedings of the 4th Workshop on Social Network Systems, (2011), Article No. 4. doi: 10.1145/1989656.1989660.

[8]

D. BrockmannL. Hufnagel and T. Geisel, The scaling laws of human travel, Nature, 439 (2006), 462-465. doi: 10.1038/nature04292.

[9]

J. Cai, Z. Li, M. Yan and Y. Li, Using crowdsourced data in location-based social networks to explore influence maximization, in Computer Communications, IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on, IEEE, 2016, 1-9. doi: 10.1109/INFOCOM.2016.7524471.

[10]

X. CaoG. Cong and C. Jensen, Mining significant semantic locations from gps data, Proc VLDB Endowment, 3 (2010), 1009-1020. doi: 10.14778/1920841.1920968.

[11]

R. ChengJ. Pang and Y. Zhang, Inferring friendship from check-in data of location-based social networks, ASONAM, (2015), 1284-1291. doi: 10.1145/2808797.2808884.

[12]

C.-Y. ChowJ. Bao and M. Mokbel, Towards location-based social networking services, ACM SIGSPATIAL International Workshop on Location Based Social Networks, (2010), 31-38. doi: 10.1145/1867699.1867706.

[13]

M. Cowles Kathryn and B. Carlin P, Markov chain monte carlo convergence diagnostics: A comparative review, Journal of the American Statistical Association, 91 (1996), 883-904. doi: 10.1080/01621459.1996.10476956.

[14]

W. Gilks, S. Richardson and D. Spiegelhalter, Markov Chain Monte Carlo in Practice, Interdisciplinary Statistics. Chapman & Hall, London, 1996.

[15]

W. GilksS. Richardson and D. Spiegelhalter, Studying convergence of markov chain monte carlo algorithms using coupled sample paths, Journal of the American Statistical Association, 91 (1996), 154-166. doi: 10.1080/01621459.1996.10476672.

[16]

L. Herlocker J, A. Konstan J, A. Borchers and J. Riedl, An algorithmic framework for performing collaborative filtering, SIGIR, 230-237.

[17]

K. KodamaY. IijimaX. Guo and Y. Ishikawa, Skyline queries based on user locations and preferences for making location-based recommendations, ACM, (2009), 9-16. doi: 10.1145/1629890.1629893.

[18]

X. LiQ. YangX. LinS. Wu and M. Wittie, itrust: Interpersonal trust measurements from social interactions, IEEE Network, 30 (2016), 54-58. doi: 10.1109/MNET.2016.7513864.

[19]

Y. Liang, Z. Cai, Q. Han and Y. Li, Location privacy leakage through sensory data, Security and Communication Networks, 2017 (2017), Article ID 7576307, 12 pages. doi: 10.1155/2017/7576307.

[20]

G. LindenB. Smith and J. York, Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Computer Society, 7 (2003), 76-80. doi: 10.1109/MIC.2003.1167344.

[21]

G. Liu, Q. Chen, Q. Yang, B. Zhu, H. Wang and W. Wang, Opinionwalk: An efficient solution to massive trust assessment in online social networks, in INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, IEEE, 2017, 1-9.

[22]

G. Liu, Q. Yang, H. Wang, X. Lin and M. P. Wittie, Assessment of multi-hop interpersonal trust in social networks by three-valued subjective logic, in INFOCOM, 2014 Proceedings IEEE, IEEE, 2014, 1698-1706. doi: 10.1109/INFOCOM.2014.6848107.

[23]

G. Liu, Q. Yang, H. Wang, S. Wu and M. P. Wittie, Uncovering the mystery of trust in an online social network, in Communications and Network Security (CNS), 2015 IEEE Conference on, IEEE, 2015, 488-496.

[24]

C.-T. LuP.-R. LeiW.-C. Peng and I.-J. Su, A framework of mining semantic regions from trajectories, International Conference on Database Systems for Advanced Applications, (2011), 193-207. doi: 10.1007/978-3-642-20149-3_16.

[25]

A. Noulas, S. Scellato, C. Mascolo and M. Pontil, Exploiting semantic annotations for clustering geographic areas and users in location-based social networks, Fifth International AAAI Conference on Weblogs and Social Media.

[26]

A. NoulasS. ScellatoN. Lathia and C. Mascolo, Mining user mobility features for next place prediction in location-based services, IEEE 12th International Conference on Data Mining, (2012), 1038-1043. doi: 10.1109/ICDM.2012.113.

[27]

I. RheeM. ShinS. HongK. LeeS. Kim Joon and S. Chong, On the levy-walk nature of human mobility, IEEE/ACM Transactions on Networking (TON), (2008). doi: 10.1109/INFOCOM.2008.145s.

[28]

L. Rossi and M. Musolesi, It's the way you check-in: Identifying users in location-based social networks, COSN, (2014), 215-226. doi: 10.1145/2660460.2660485.

[29]

J. Sang, T. Mei and C. Xu, Activity sensor: Check-in usage mining for local recommendation, ACM Transactions on Intelligent Systems and Technology (TIST), 6 (2015), Article No. 41. doi: 10.1145/2700468.

[30]

B. SarwarG. KarypisJ. Konstan and J. Riedl, Item-based collaborative filtering recommendation algorithms, WWW '01 Proceedings of the 10th International Conference on World Wide Web, (2001), 285-295. doi: 10.1145/371920.372071.

[31]

M. SarwatJ. Levandoski J.A. Eldawy and M. Mokbel, Lars*: An efficient and scalable location-aware recommender system, Transactions ON Knowledge and Data Engineering, 26 (2014), 1384-1399. doi: 10.1109/TKDE.2013.29.

[32]

X. Su and K. Taghi M., A survey of collaborative filtering techniques, Advances in Artificial Intelligence, 2009 (2009), Article ID 421425, 19 pages. doi: 10.1155/2009/421425.

[33]

Z. SuY. Hui and Q. Yang, The next generation vehicular networks: A content-centric framework, IEEE Wireless Communications, 24 (2017), 60-66. doi: 10.1109/MWC.2017.1600195WC.

[34]

Z. SuQ. XuF. HouQ. Yang and Q. Qi, Edge caching for layered video contents in mobile social networks, IEEE Transactions on Multimedia, 19 (2017), 2210-2221. doi: 10.1109/TMM.2017.2733338.

[35]

Y. WangG. YinZ. CaiY. Dong and H. Dong, A trust-based probabilistic recommendation model for social networks, Journal of Network and Computer Applications, 55 (2015), 59-67. doi: 10.1016/j.jnca.2015.04.007.

[36]

Y. WangN. Yuan JingD. LianL. XuX. XieE. Chen and Y. Rui, Regularity and conformity: Location prediction using heterogeneous mobility data, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), 1275-1284. doi: 10.1145/2783258.2783350.

[37]

J. Weston, C. Wang, R. Weiss and A. Berenzweig, Latent collaborative retrieval, Proceedings of the 29th International Conference on Machine Learning, 9-16.

[38]

X. XiaoQ. ZhengYu andLuo and X. Xie, Finding similar users using category-based location history, GIS, (2010), 442-445. doi: 10.1145/1869790.1869857.

[39]

L. XiongX. ChenT.-K. HuangJ. Schneider G and G. Carbonell, Temporal collaborative filtering with bayesian probabilistic tensor factorization, Siam International Conference on Data Mining, (2010), 211-222. doi: 10.1137/1.9781611972801.19.

[40]

D. Yang, D. Zhang, V. Zheng W and Z. Yu, Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 129-142. doi: 10.1109/GLOCOM.2010.5684166.

[41]

Q. Yang, A. Lim, X. Ruan and X. Qin, Location privacy protection in contention based forwarding for vanets, in Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, IEEE, 2010, 1-5. doi: 10.1109/GLOCOM.2010.5684166.

[42]

Q. YangA. LimX. RuanX. Qin and D. Kim, Location-preserved contention-based routing in vehicular ad hoc networks, Security and Communication Networks, 9 (2016), 886-898. doi: 10.1002/sec.1008.

[43]

Q. Yang and H. Wang, Toward trustworthy vehicular social networks, IEEE Communications Magazine, 53 (2015), 42-47.

[44]

Q. YangB. Zhu and S. Wu, Wu, An architecture of cloud-assisted information dissemination in vehicular networks, IEEE Access, 4 (2016), 2764-2770. doi: 10.1109/ACCESS.2016.2572206.

[45]

M. YeP. YinW.-C. Lee and D.-L. Lee, Exploiting geographical influence for collaborative point-of-interest recommendation, ACM SIGIR Conference on Research and Development in Information Retrieval, (2011), 325-334. doi: 10.1145/2009916.2009962.

[46]

H. YinB. CuiL. ChenZ. Hu and X. Zhou, Dynamic user modeling in social media systems, ACM Transactions on Information Systems (TOIS), 33 (2015), p10. doi: 10.1145/2699670.

[47]

H. YinB. CuiY. SunZ. Hu and L. Chen, Lcars: A spatial item recommender system, ACM Transactions on Information Systems (TOIS), 32 (2014), p11. doi: 10.1145/2629461.

[48]

Y. Yu and X. Chen, A survey of point-of-interest recommendation in location-based social networks, Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence.

[49]

J. YuanY. Zheng and X. Xie, Discovering regions of different functions in a city using human mobility and pois, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2012), 186-194. doi: 10.1145/2339530.2339561.

[50]

Q. YuanG. CongZ. MaA. Sun and N. M. Thalmann, Time-aware point-of-interest recommendation, ACM SIGIR, (2013), 363-372. doi: 10.1145/2484028.2484030.

[51]

Q. YuanG. Cong and A. Sun, Graph-based point-of-interest recommendation with geographical and temporal influences, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, (2014), 659-668. doi: 10.1145/2661829.2661983.

[52]

F. ZhangJ. YuanD. LianX. Xie and W.-Y. Ma, Collaborative knowledge base embedding for recommender systems, KDD, (2016), 353-362. doi: 10.1145/2939672.2939673.

[53]

J. ZhangY. Chow and C. Li, igeorec: A personalized and efficient geographical location recommendation framework, IEEE Transaction on Service Computing, 8 (2015), 701-714. doi: 10.1109/TSC.2014.2328341.

[54]

J.-D. Zhang and C.-Y. Chow, Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations, SIGIR, (2015), 443-452. doi: 10.1145/2766462.2767711.

[55]

J.-D. Zhang and C.-Y. Chow, igslr: personalized geo-social location recommendation: A kernel density estimation approach, n Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, (2013), 334-343. doi: 10.1145/2525314.2525339.

[56]

J.-D. ZhangC.-Y. Chow and Y. Zheng, Orec: An opinion-based point-of-interest recommendation framework, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, (2015), 1641-1650. doi: 10.1145/2806416.2806516.

[57]

W. Zhang and J. Wang, Location and time aware social collaborative retrieval for new successive point-of-interest recommendation, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, (2015), 1221-1230. doi: 10.1145/2806416.2806564.

[58]

Y. ZhangZ. Zheng and M. Lyu, Wspred: A time-aware personalized qos prediction framework for web services, Software Reliability Engineering (ISSRE), (2011), 210-219. doi: 10.1109/ISSRE.2011.17.

[59]

S. ZhaoI. King and M. Lyu, Capturing geographical influence in poi recommendations, International Conference on Neural Information Processing, (2013), 530-537. doi: 10.1007/978-3-642-42042-9_66.

[60]

S. Zhao, H. Zhao Tong anf Yang, M. Lyu R and I. King, Stellar: Spatial-temporal latent ranking for successive point-of-interest recommendation, Thirtieth AAAI Conference on Artificial Intelligence.

[61]

V. Zheng M., Y. Zheng, X. Xie and Q. Yang, Collaborative location and activity recommendations with gps history data, WWW.

[62]

X. Zheng, Z. Cai, J. Li and H. Gao, Location-privacy-aware review publication mechanism for local business service systems, in INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, IEEE, 2017, 1-9. doi: 10.1109/INFOCOM.2017.8056976.

[63]

Y. ZhengL. ZhangX. Xie and W.-Y. Ma, Mining correlation between locations using human location history, Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, (2009), 472-475. doi: 10.1145/1653771.1653847.

show all references

References:
[1]

G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Trans Knowl Data Eng, 734-749.

[2]

Y. AraseX. XieM. DuanT. Hara and S. Nishio, A game based approach to assign geographical relevance to web images, Proceedings of the 18th International Conference on World Wide Web, (2009), 811-820. doi: 10.1145/1526709.1526818.

[3]

Y. AraseX. XieT. Hara and S. Nishio, Mining people's trips from large scale geo-tagged photos, Proceedings of the International Conference on Multimedia, (2010), 133-142. doi: 10.1145/1873951.1873971.

[4]

M. Attarl, M. Manguoglu, I. Toroslu H, P. Symeonidis, P. Senkul and Y. Manolopoulos, Geoactivity recommendations by using improved feature combination, Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 996-1003.

[5]

L. Backstrom and J. Leskovec, Supervised random walks: Predicting and recommending links in social networks, ACM, (2011), 635-644. doi: 10.1145/1935826.1935914.

[6]

J. BaoY. Zheng and M. Mokbel, Location-based and preference-aware recommendation using sparse geo-social networking data, SIGSPATIAL '12 Proceedings of the 20th International Conference on Advances in Geographic Information Systems, (2012), 199-208. doi: 10.1145/2424321.2424348.

[7]

B. Berjani and T. Strufe, A recommendation system for spots in location-based online social networks, Proceedings of the 4th Workshop on Social Network Systems, (2011), Article No. 4. doi: 10.1145/1989656.1989660.

[8]

D. BrockmannL. Hufnagel and T. Geisel, The scaling laws of human travel, Nature, 439 (2006), 462-465. doi: 10.1038/nature04292.

[9]

J. Cai, Z. Li, M. Yan and Y. Li, Using crowdsourced data in location-based social networks to explore influence maximization, in Computer Communications, IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on, IEEE, 2016, 1-9. doi: 10.1109/INFOCOM.2016.7524471.

[10]

X. CaoG. Cong and C. Jensen, Mining significant semantic locations from gps data, Proc VLDB Endowment, 3 (2010), 1009-1020. doi: 10.14778/1920841.1920968.

[11]

R. ChengJ. Pang and Y. Zhang, Inferring friendship from check-in data of location-based social networks, ASONAM, (2015), 1284-1291. doi: 10.1145/2808797.2808884.

[12]

C.-Y. ChowJ. Bao and M. Mokbel, Towards location-based social networking services, ACM SIGSPATIAL International Workshop on Location Based Social Networks, (2010), 31-38. doi: 10.1145/1867699.1867706.

[13]

M. Cowles Kathryn and B. Carlin P, Markov chain monte carlo convergence diagnostics: A comparative review, Journal of the American Statistical Association, 91 (1996), 883-904. doi: 10.1080/01621459.1996.10476956.

[14]

W. Gilks, S. Richardson and D. Spiegelhalter, Markov Chain Monte Carlo in Practice, Interdisciplinary Statistics. Chapman & Hall, London, 1996.

[15]

W. GilksS. Richardson and D. Spiegelhalter, Studying convergence of markov chain monte carlo algorithms using coupled sample paths, Journal of the American Statistical Association, 91 (1996), 154-166. doi: 10.1080/01621459.1996.10476672.

[16]

L. Herlocker J, A. Konstan J, A. Borchers and J. Riedl, An algorithmic framework for performing collaborative filtering, SIGIR, 230-237.

[17]

K. KodamaY. IijimaX. Guo and Y. Ishikawa, Skyline queries based on user locations and preferences for making location-based recommendations, ACM, (2009), 9-16. doi: 10.1145/1629890.1629893.

[18]

X. LiQ. YangX. LinS. Wu and M. Wittie, itrust: Interpersonal trust measurements from social interactions, IEEE Network, 30 (2016), 54-58. doi: 10.1109/MNET.2016.7513864.

[19]

Y. Liang, Z. Cai, Q. Han and Y. Li, Location privacy leakage through sensory data, Security and Communication Networks, 2017 (2017), Article ID 7576307, 12 pages. doi: 10.1155/2017/7576307.

[20]

G. LindenB. Smith and J. York, Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Computer Society, 7 (2003), 76-80. doi: 10.1109/MIC.2003.1167344.

[21]

G. Liu, Q. Chen, Q. Yang, B. Zhu, H. Wang and W. Wang, Opinionwalk: An efficient solution to massive trust assessment in online social networks, in INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, IEEE, 2017, 1-9.

[22]

G. Liu, Q. Yang, H. Wang, X. Lin and M. P. Wittie, Assessment of multi-hop interpersonal trust in social networks by three-valued subjective logic, in INFOCOM, 2014 Proceedings IEEE, IEEE, 2014, 1698-1706. doi: 10.1109/INFOCOM.2014.6848107.

[23]

G. Liu, Q. Yang, H. Wang, S. Wu and M. P. Wittie, Uncovering the mystery of trust in an online social network, in Communications and Network Security (CNS), 2015 IEEE Conference on, IEEE, 2015, 488-496.

[24]

C.-T. LuP.-R. LeiW.-C. Peng and I.-J. Su, A framework of mining semantic regions from trajectories, International Conference on Database Systems for Advanced Applications, (2011), 193-207. doi: 10.1007/978-3-642-20149-3_16.

[25]

A. Noulas, S. Scellato, C. Mascolo and M. Pontil, Exploiting semantic annotations for clustering geographic areas and users in location-based social networks, Fifth International AAAI Conference on Weblogs and Social Media.

[26]

A. NoulasS. ScellatoN. Lathia and C. Mascolo, Mining user mobility features for next place prediction in location-based services, IEEE 12th International Conference on Data Mining, (2012), 1038-1043. doi: 10.1109/ICDM.2012.113.

[27]

I. RheeM. ShinS. HongK. LeeS. Kim Joon and S. Chong, On the levy-walk nature of human mobility, IEEE/ACM Transactions on Networking (TON), (2008). doi: 10.1109/INFOCOM.2008.145s.

[28]

L. Rossi and M. Musolesi, It's the way you check-in: Identifying users in location-based social networks, COSN, (2014), 215-226. doi: 10.1145/2660460.2660485.

[29]

J. Sang, T. Mei and C. Xu, Activity sensor: Check-in usage mining for local recommendation, ACM Transactions on Intelligent Systems and Technology (TIST), 6 (2015), Article No. 41. doi: 10.1145/2700468.

[30]

B. SarwarG. KarypisJ. Konstan and J. Riedl, Item-based collaborative filtering recommendation algorithms, WWW '01 Proceedings of the 10th International Conference on World Wide Web, (2001), 285-295. doi: 10.1145/371920.372071.

[31]

M. SarwatJ. Levandoski J.A. Eldawy and M. Mokbel, Lars*: An efficient and scalable location-aware recommender system, Transactions ON Knowledge and Data Engineering, 26 (2014), 1384-1399. doi: 10.1109/TKDE.2013.29.

[32]

X. Su and K. Taghi M., A survey of collaborative filtering techniques, Advances in Artificial Intelligence, 2009 (2009), Article ID 421425, 19 pages. doi: 10.1155/2009/421425.

[33]

Z. SuY. Hui and Q. Yang, The next generation vehicular networks: A content-centric framework, IEEE Wireless Communications, 24 (2017), 60-66. doi: 10.1109/MWC.2017.1600195WC.

[34]

Z. SuQ. XuF. HouQ. Yang and Q. Qi, Edge caching for layered video contents in mobile social networks, IEEE Transactions on Multimedia, 19 (2017), 2210-2221. doi: 10.1109/TMM.2017.2733338.

[35]

Y. WangG. YinZ. CaiY. Dong and H. Dong, A trust-based probabilistic recommendation model for social networks, Journal of Network and Computer Applications, 55 (2015), 59-67. doi: 10.1016/j.jnca.2015.04.007.

[36]

Y. WangN. Yuan JingD. LianL. XuX. XieE. Chen and Y. Rui, Regularity and conformity: Location prediction using heterogeneous mobility data, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), 1275-1284. doi: 10.1145/2783258.2783350.

[37]

J. Weston, C. Wang, R. Weiss and A. Berenzweig, Latent collaborative retrieval, Proceedings of the 29th International Conference on Machine Learning, 9-16.

[38]

X. XiaoQ. ZhengYu andLuo and X. Xie, Finding similar users using category-based location history, GIS, (2010), 442-445. doi: 10.1145/1869790.1869857.

[39]

L. XiongX. ChenT.-K. HuangJ. Schneider G and G. Carbonell, Temporal collaborative filtering with bayesian probabilistic tensor factorization, Siam International Conference on Data Mining, (2010), 211-222. doi: 10.1137/1.9781611972801.19.

[40]

D. Yang, D. Zhang, V. Zheng W and Z. Yu, Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 129-142. doi: 10.1109/GLOCOM.2010.5684166.

[41]

Q. Yang, A. Lim, X. Ruan and X. Qin, Location privacy protection in contention based forwarding for vanets, in Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, IEEE, 2010, 1-5. doi: 10.1109/GLOCOM.2010.5684166.

[42]

Q. YangA. LimX. RuanX. Qin and D. Kim, Location-preserved contention-based routing in vehicular ad hoc networks, Security and Communication Networks, 9 (2016), 886-898. doi: 10.1002/sec.1008.

[43]

Q. Yang and H. Wang, Toward trustworthy vehicular social networks, IEEE Communications Magazine, 53 (2015), 42-47.

[44]

Q. YangB. Zhu and S. Wu, Wu, An architecture of cloud-assisted information dissemination in vehicular networks, IEEE Access, 4 (2016), 2764-2770. doi: 10.1109/ACCESS.2016.2572206.

[45]

M. YeP. YinW.-C. Lee and D.-L. Lee, Exploiting geographical influence for collaborative point-of-interest recommendation, ACM SIGIR Conference on Research and Development in Information Retrieval, (2011), 325-334. doi: 10.1145/2009916.2009962.

[46]

H. YinB. CuiL. ChenZ. Hu and X. Zhou, Dynamic user modeling in social media systems, ACM Transactions on Information Systems (TOIS), 33 (2015), p10. doi: 10.1145/2699670.

[47]

H. YinB. CuiY. SunZ. Hu and L. Chen, Lcars: A spatial item recommender system, ACM Transactions on Information Systems (TOIS), 32 (2014), p11. doi: 10.1145/2629461.

[48]

Y. Yu and X. Chen, A survey of point-of-interest recommendation in location-based social networks, Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence.

[49]

J. YuanY. Zheng and X. Xie, Discovering regions of different functions in a city using human mobility and pois, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2012), 186-194. doi: 10.1145/2339530.2339561.

[50]

Q. YuanG. CongZ. MaA. Sun and N. M. Thalmann, Time-aware point-of-interest recommendation, ACM SIGIR, (2013), 363-372. doi: 10.1145/2484028.2484030.

[51]

Q. YuanG. Cong and A. Sun, Graph-based point-of-interest recommendation with geographical and temporal influences, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, (2014), 659-668. doi: 10.1145/2661829.2661983.

[52]

F. ZhangJ. YuanD. LianX. Xie and W.-Y. Ma, Collaborative knowledge base embedding for recommender systems, KDD, (2016), 353-362. doi: 10.1145/2939672.2939673.

[53]

J. ZhangY. Chow and C. Li, igeorec: A personalized and efficient geographical location recommendation framework, IEEE Transaction on Service Computing, 8 (2015), 701-714. doi: 10.1109/TSC.2014.2328341.

[54]

J.-D. Zhang and C.-Y. Chow, Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations, SIGIR, (2015), 443-452. doi: 10.1145/2766462.2767711.

[55]

J.-D. Zhang and C.-Y. Chow, igslr: personalized geo-social location recommendation: A kernel density estimation approach, n Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, (2013), 334-343. doi: 10.1145/2525314.2525339.

[56]

J.-D. ZhangC.-Y. Chow and Y. Zheng, Orec: An opinion-based point-of-interest recommendation framework, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, (2015), 1641-1650. doi: 10.1145/2806416.2806516.

[57]

W. Zhang and J. Wang, Location and time aware social collaborative retrieval for new successive point-of-interest recommendation, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, (2015), 1221-1230. doi: 10.1145/2806416.2806564.

[58]

Y. ZhangZ. Zheng and M. Lyu, Wspred: A time-aware personalized qos prediction framework for web services, Software Reliability Engineering (ISSRE), (2011), 210-219. doi: 10.1109/ISSRE.2011.17.

[59]

S. ZhaoI. King and M. Lyu, Capturing geographical influence in poi recommendations, International Conference on Neural Information Processing, (2013), 530-537. doi: 10.1007/978-3-642-42042-9_66.

[60]

S. Zhao, H. Zhao Tong anf Yang, M. Lyu R and I. King, Stellar: Spatial-temporal latent ranking for successive point-of-interest recommendation, Thirtieth AAAI Conference on Artificial Intelligence.

[61]

V. Zheng M., Y. Zheng, X. Xie and Q. Yang, Collaborative location and activity recommendations with gps history data, WWW.

[62]

X. Zheng, Z. Cai, J. Li and H. Gao, Location-privacy-aware review publication mechanism for local business service systems, in INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, IEEE, 2017, 1-9. doi: 10.1109/INFOCOM.2017.8056976.

[63]

Y. ZhengL. ZhangX. Xie and W.-Y. Ma, Mining correlation between locations using human location history, Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, (2009), 472-475. doi: 10.1145/1653771.1653847.

Figure 1.  Overview a location-based social network
Figure 2.  Influence of training size to the precision
Figure 3.  Cosine similarity between the transition and similarity matrix
Figure 4.  Average number of new visited location changes through each month
Figure 5.  Cumulative distribution function (CDF) of time interval between timelinesss
Figure 6.  Precision and recall of CF with or without K-means clustering and SVD
Figure 7.  Influence of data sparsity and category to the recommendation rate (recall ratio) where number of recommendations $k = 20$
Figure 8.  Influence of constant and dynamic similarity to the recommendation rate (precision ratio) where $N$ is number of multiplications
Figure 9.  Recommendation timeliness comparison for constant similarity matrix and dynamic similarity matrix
Figure 10.  Similarity of the eigenvector of transition matrix and similarity matrix comparisons of our method and the three benchmarks
Figure 11.  The recommendation rate of our method and the three baseline varying in the recommendation timeliness (month)
Figure 12.  The empirical CDF of time intervals of our method and the three benchmarks except CF varying in the recommendation period time (month)
Figure 13.  Precisions and recalls of LAR, CF, LGS and GeoSoCa
Table 1.  An example of user-item matrix
Apple Pear Grape Watermelon
Alice Like Like Dislike Dislike
Bob Dislike Like Like
Chris Dislike Like
Tony Like Dislike
Apple Pear Grape Watermelon
Alice Like Like Dislike Dislike
Bob Dislike Like Like
Chris Dislike Like
Tony Like Dislike
Table 2.  A summary of precision and recall ratios of the four approaches
Models Precision Recall Avg.Precision Avg.Recall
CF 0.51 0.69 0.3573 0.6395
LGS 0.59 0.77 0.3682 0.6762
GeoSoCa 0.58 0.76 0.3657 0.6684
LAR 0.62 0.83 0.4582 0.7523
Models Precision Recall Avg.Precision Avg.Recall
CF 0.51 0.69 0.3573 0.6395
LGS 0.59 0.77 0.3682 0.6762
GeoSoCa 0.58 0.76 0.3657 0.6684
LAR 0.62 0.83 0.4582 0.7523
[1]

Wei Fu, Jun Liu, Yirong Lai. Collaborative filtering recommendation algorithm towards intelligent community. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 811-822. doi: 10.3934/dcdss.2019054

[2]

Boran Hu, Zehui Cheng, Zhangbing Zhou. Web services recommendation leveraging semantic similarity computing. Mathematical Foundations of Computing, 2018, 1 (2) : 101-119. doi: 10.3934/mfc.2018006

[3]

Werner Creixell, Juan Carlos Losada, Tomás Arredondo, Patricio Olivares, Rosa María Benito. Serendipity in social networks. Networks & Heterogeneous Media, 2012, 7 (3) : 363-371. doi: 10.3934/nhm.2012.7.363

[4]

Dandan Hu, Zhi-Wei Liu. Location and capacity design of congested intermediate facilities in networks. Journal of Industrial & Management Optimization, 2016, 12 (2) : 449-470. doi: 10.3934/jimo.2016.12.449

[5]

Sharon M. Cameron, Ariel Cintrón-Arias. Prisoner's Dilemma on real social networks: Revisited. Mathematical Biosciences & Engineering, 2013, 10 (5&6) : 1381-1398. doi: 10.3934/mbe.2013.10.1381

[6]

Robin Cohen, Alan Tsang, Krishna Vaidyanathan, Haotian Zhang. Analyzing opinion dynamics in online social networks. Big Data & Information Analytics, 2016, 1 (4) : 279-298. doi: 10.3934/bdia.2016011

[7]

Zuguo Chen, Yonggang Li, Xiaofang Chen, Chunhua Yang, Weihua Gui. Anode effect prediction based on collaborative two-dimensional forecast model in aluminum electrolysis production. Journal of Industrial & Management Optimization, 2018, 13 (5) : 1-24. doi: 10.3934/jimo.2018060

[8]

Dariusz Borkowski. Forward and backward filtering based on backward stochastic differential equations. Inverse Problems & Imaging, 2016, 10 (2) : 305-325. doi: 10.3934/ipi.2016002

[9]

Massimiliano Caramia, Giovanni Storchi. Evaluating the effects of parking price and location in multi-modal transportation networks. Networks & Heterogeneous Media, 2006, 1 (3) : 441-465. doi: 10.3934/nhm.2006.1.441

[10]

Guowei Dai, Ruyun Ma, Haiyan Wang, Feng Wang, Kuai Xu. Partial differential equations with Robin boundary condition in online social networks. Discrete & Continuous Dynamical Systems - B, 2015, 20 (6) : 1609-1624. doi: 10.3934/dcdsb.2015.20.1609

[11]

Lea Ellwardt, Penélope Hernández, Guillem Martínez-Cánovas, Manuel Muñoz-Herrera. Conflict and segregation in networks: An experiment on the interplay between individual preferences and social influence. Journal of Dynamics & Games, 2016, 3 (2) : 191-216. doi: 10.3934/jdg.2016010

[12]

Jingli Ren, Dandan Zhu, Haiyan Wang. Spreading-vanishing dichotomy in information diffusion in online social networks with intervention. Discrete & Continuous Dynamical Systems - B, 2017, 22 (11) : 1-23. doi: 10.3934/dcdsb.2018240

[13]

Weiping Li, Haiyan Wu, Jie Yang. Intelligent recognition algorithm for social network sensitive information based on classification technology. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 1385-1398. doi: 10.3934/dcdss.2019095

[14]

Nicolás M. Crisosto, Christopher M. Kribs-Zaleta, Carlos Castillo-Chávez, Stephen Wirkus. Community resilience in collaborative learning. Discrete & Continuous Dynamical Systems - B, 2010, 14 (1) : 17-40. doi: 10.3934/dcdsb.2010.14.17

[15]

Shuai Ren, Tao Zhang, Fangxia Shi. Characteristic analysis of carrier based on the filtering and a multi-wavelet method for the information hiding. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1291-1299. doi: 10.3934/dcdss.2015.8.1291

[16]

Junbo Jia, Zhen Jin, Lili Chang, Xinchu Fu. Structural calculations and propagation modeling of growing networks based on continuous degree. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1215-1232. doi: 10.3934/mbe.2017062

[17]

Mahdi Jalili. EEG-based functional brain networks: Hemispheric differences in males and females. Networks & Heterogeneous Media, 2015, 10 (1) : 223-232. doi: 10.3934/nhm.2015.10.223

[18]

Seunghee Lee, Ganguk Hwang. A new analytical model for optimized cognitive radio networks based on stochastic geometry. Journal of Industrial & Management Optimization, 2017, 13 (4) : 1883-1899. doi: 10.3934/jimo.2017023

[19]

Murat Arcak, Eduardo D. Sontag. A passivity-based stability criterion for a class of biochemical reaction networks. Mathematical Biosciences & Engineering, 2008, 5 (1) : 1-19. doi: 10.3934/mbe.2008.5.1

[20]

Huajun Tang, T. C. Edwin Cheng, Chi To Ng. A note on the subtree ordered median problem in networks based on nestedness property. Journal of Industrial & Management Optimization, 2012, 8 (1) : 41-49. doi: 10.3934/jimo.2012.8.41

 Impact Factor: 

Metrics

  • PDF downloads (34)
  • HTML views (473)
  • Cited by (0)

Other articles
by authors

[Back to Top]