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Research Papers

Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graph

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References

  • Broccolo, D., et al., 2012. Generating suggestions for queries in the long tail with an inverted index. Information Processing & Management, 48 (2), 326–339. doi:10.1016/j.ipm.2011.07.005
  • Chen, J., et al., 2016. Effective successive POI recommendation inferred with individual behavior and group preference. Neurocomputing, 210, 174–184. doi:10.1016/j.neucom.2015.10.146
  • Chu, M.T. and Guo, Q., 1998. A numerical method for the inverse stochastic spectrum problem. SIAM Journal on Matrix Analysis and Applications, 19 (4), 1027–1039. doi:10.1137/S0895479896292418
  • Ding, Y. and Li, X., 2005. Time weight collaborative filtering. Paper presented at the proceedings of the 14th ACM international conference on information and knowledge management. Bremen, Germany.
  • Gao, H., et al., 2013. Exploring temporal effects for location recommendation on location-based social networks. Paper presented at the proceedings of the 7th ACM conference on recommender systems. Hong Kong, China.
  • Gao, H., Tang, J., and Liu, H., 2012. gSCorr: modeling geo-social correlations for new check-ins on location-based social networks. Paper presented at the proceedings of the 21st ACM international conference on information and knowledge management. Maui Hawaii, USA.
  • Guo, T., et al., 2018. Differentially private graph-link analysis based social recommendation. Information Sciences, 463, 214–226. doi:10.1016/j.ins.2018.06.054
  • Kant, S. and Mahara, T., 2018. Nearest biclusters collaborative filtering framework with fusion. Journal of Computational Science, 25, 204–212. doi:10.1016/j.jocs.2017.03.018
  • Kefalas, P. and Manolopoulos, Y., 2017. A time-aware spatio-textual recommender system. Expert Systems with Applications, 78, 396–406. doi:10.1016/j.eswa.2017.01.060
  • Kefalas, P., Symeonidis, P., and Manolopoulos, Y., 2018. Recommendations based on a heterogeneous spatio-temporal social network. World Wide Web, 21 (2), 345–371. doi:10.1007/s11280-017-0454-0
  • Konstas, I., Stathopoulos, V., and Jose, J.M., 2009. On social networks and collaborative recommendation. Paper presented at the proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval. Boston, MA.
  • Koren, Y., 2009. Collaborative filtering with temporal dynamics. Paper presented at the proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. Paris France.
  • Lee, S., et al., 2011. Random walk based entity ranking on graph for multidimensional recommendation. Paper presented at the proceedings of the fifth ACM conference on recommender systems. Chicago, IL.
  • Li, Y., et al., 2014. Spatial-aware interest group queries in location-based social networks. Data & Knowledge Engineering, 92, 20–38. doi:10.1016/j.datak.2014.06.001
  • Litou, I., Boutsis, I., and Kalogeraki, V., 2017. Efficient techniques for time-constrained information dissemination using location-based social networks. Information Systems, 64, 321–349. doi:10.1016/j.is.2015.12.002
  • Liu, S., et al., 2019. Evolving graph construction for successive recommendation in event-based social networks. Future Generation Computer Systems,96, 502–551.
  • Lovász, L., 1993. Random walks on graphs: a survey. Combinatorics, Paul erdos is eighty, 2 (1), 1–46.
  • Lu, J. and Wang, H., 2014. Variance reduction in large graph sampling. Information Processing & Management, 50 (3), 476–491. doi:10.1016/j.ipm.2014.02.003
  • Luo, L., et al., 2019. Personalized recommendation by matrix co-factorization with tags and time information. Expert Systems with Applications, 119, 311–321. doi:10.1016/j.eswa.2018.11.003
  • Lyu, Y., et al., 2019. iMCRec: a Multi-Criteria Framework for Personalized Point-of-Interest Recommendations. Information Sciences, 483, 294–312.
  • Melville, P., Mooney, R.J., and Nagarajan, R., 2002. Content-boosted collaborative filtering for improved recommendations. Aaai/iaai, 23, 187–192.
  • Musto, C., et al., 2017. Introducing linked open data in graph-based recommender systems. Information Processing & Management, 53 (2), 405–435. doi:10.1016/j.ipm.2016.12.003
  • Najafabadi, M.K., Mohamed, A., and Onn, C.W., 2019. An impact of time and item influencer in collaborative filtering recommendations using graph-based model. Information Processing & Management, 56 (3), 526–540. doi:10.1016/j.ipm.2018.12.007
  • Niu, J., et al., 2016. FUIR: fusing user and item information to deal with data sparsity by using side information in recommendation systems. Journal of Network and Computer Applications, 70, 41–50. doi:10.1016/j.jnca.2016.05.006
  • Noulas, A., et al., 2012. A random walk around the city: new venue recommendation in location-based social networks. Paper presented at the privacy, security, risk and trust (PASSAT), 2012 international conference on and 2012 international confernece on social computing (socialcom). Amsterdam, Netherlands.
  • Nzeko’O, A.J.N., Tchuente, M., and Latapy, M., 2017. Time weight content-based extensions of temporal graphs for personalized recommendation. Paper presented at the WEBIST 2017-13th international conference on web information systems and technologies. Porto, Portugal.
  • Orso, V., et al., 2017. Overlaying social information: the effects on users’ search and information-selection behavior. Information Processing & Management, 53 (6), 1269–1286. doi:10.1016/j.ipm.2017.06.001
  • Pongnumkul, S. and Motohashi, K., 2015. Random walk-based recommendation with restart using social information and bayesian transition matrices. International Journal of Computer Applications, 114 (9), 32–38. doi:10.5120/20009-1960
  • Popescul, A., Pennock, D.M., and Lawrence, S., 2001. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. Paper presented at the proceedings of the seventeenth conference on uncertainty in artificial intelligence. San Francisco, CA: USA.
  • Qi, L., et al., 2019. Time-aware distributed service recommendation with privacy-preservation. Information Sciences, 480, 354–364. doi:10.1016/j.ins.2018.11.030
  • Rafailidis, D., Kefalas, P., and Manolopoulos, Y., 2017. Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Systems with Applications, 74, 11–18. doi:10.1016/j.eswa.2017.01.005
  • Ren, X., et al., 2017. Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. Neurocomputing, 241, 38–55. doi:10.1016/j.neucom.2017.02.005
  • Si, Y., Zhang, F., and Liu, W., 2017. CTF-ARA: an adaptive method for POI recommendation based on check-in and temporal features. Knowledge-Based Systems, 128, 59–70. doi:10.1016/j.knosys.2017.04.013
  • Si, Y., Zhang, F., and Liu, W., 2019. An adaptive point-of-interest recommendation method for location-based social networks based on user activity and spatial features. Knowledge-Based Systems, 163, 267–282. doi:10.1016/j.knosys.2018.08.031
  • Sun, G., et al., 2019. Towards privacy preservation for “check-in” services in location-based social networks. Information Sciences, 481, 616–634. doi:10.1016/j.ins.2019.01.008
  • Sun, Y., et al., 2015. Location information disclosure in location-based social network services: privacy calculus, benefit structure, and gender differences. Computers in Human Behavior, 52, 278–292. doi:10.1016/j.chb.2015.06.006
  • Tong, H., Faloutsos, C., and Pan, J.-Y., 2006. Fast random walk with restart and its applications. Paper presented at the sixth international conference on data mining (ICDM’06). Hong Kong, China.
  • Valverde-Rebaza, J.C., et al., 2018. The role of location and social strength for friendship prediction in location-based social networks. Information Processing & Management, 54 (4), 475–489. doi:10.1016/j.ipm.2018.02.004
  • Wang, W., et al., 2017. ST-SAGE: a spatial-temporal sparse additive generative model for spatial item recommendation. ACM Transactions on Intelligent Systems and Technology (TIST), 8 (3), 48.
  • Xia, B., et al., 2017. Vrer: context-based venue recommendation using embedded space ranking SVM in location-based social network. Expert Systems with Applications, 83, 18–29. doi:10.1016/j.eswa.2017.04.020
  • Xiang, L., et al., 2010. Temporal recommendation on graphs via long-and short-term preference fusion. Paper presented at the proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. Washington DC.
  • Xie, M., et al., 2016. Learning graph-based POI embedding for location-based recommendation. Paper presented at the proceedings of the 25th ACM international on conference on information and knowledge management. Indianapolis, USA.
  • Yang, X., et al., 2014. A survey of collaborative filtering based social recommender systems. Computer Communications, 41, 1–10. doi:10.1016/j.comcom.2013.06.009
  • Yang, Y., Hooshyar, D., and Lim, H.S., 2019. GPS: factorized group preference-based similarity models for sparse sequential recommendation. Information Sciences, 481, 394–411. doi:10.1016/j.ins.2018.12.053
  • Ying, Y., Chen, L., and Chen, G., 2017. A temporal-aware POI recommendation system using context-aware tensor decomposition and weighted HITS. Neurocomputing, 242, 195–205. doi:10.1016/j.neucom.2017.02.067
  • Yu, C., et al., 2017a. Using check-in features to partition locations for individual users in location based social network. Information Fusion, 37, 86–97. doi:10.1016/j.inffus.2017.01.006
  • Yu, J., Shen, Y., and Yang, Z., 2014. Topic-STG: extending the session-based temporal graph approach for personalized tweet recommendation. Paper presented at the proceedings of the 23rd international conference on world wide web. Seoul, Republic of Korea.
  • Yu, L., et al., 2017b. TIIREC: a tensor approach for tag-driven item recommendation with sparse user generated content. Information Sciences, 411, 122–135. doi:10.1016/j.ins.2017.05.025
  • Yuan, Q., Cong, G., and Sun, A., 2014. Graph-based point-of-interest recommendation with geographical and temporal influences. Paper presented at the proceedings of the 23rd ACM international conference on conference on information and knowledge management. Shanghai, China.
  • Zhang, C., et al., 2014. Latent factor transition for dynamic collaborative filtering. Paper presented at the proceedings of the 2014 SIAM international conference on data mining. Philadelphia, Pennsylvania.
  • Zhang, S. and Cheng, H., 2018, Exploiting Context Graph Attention for POI Recommendation in Location-Based Social Networks. In: Pei J., Manolopoulos Y., Sadiq S., Li J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science, vol 10827. Springer, Cham.
  • Zhao, S., et al., 2016. STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation. Paper presented at the thirtieth AAAI conference on artificial intelligence. Phoenix, Arizona USA
  • Zhou, W. and Han, W., 2019. Personalized recommendation via user preference matching. Information Processing & Management, 56 (3), 955–968. doi:10.1016/j.ipm.2019.02.002