User and Location Based Collaborative Filtering Recommendation in Social Networks

Authors(2) :-T. Manjula, P. S. Naveen Kumar

The social networks are utilized to share data between the clients. The Location Based Social Networks (LBSN) is examined with location and time data on check in points of interest. The administration rating forecast operations are completed with the client location and audit points of interest. The Location Based Rating Prediction (LBRP) algorithm is connected for the administration rating expectation process. The LBRP utilizes the three estimations for the forecast procedure. The client thing geological associations, client land associations and relational intrigue similitude measures are assessed for the rating forecast process. The client thing geological association demonstrates the separation between the client appraisals and the client thing land locations. The client client land association shows the client rating contrasts and the client client geographical location separations. The administration rating expectation process is developed with client conduct disclosure and administration score file strategies. The client conduct estimation process is done with multi movement focuses. The administration score estimation and file operations are performed with the characteristics of the Point of Interests (POI). The client proposal assignment is coordinated with the framework to recommend better administrations with reference to the client conduct and evaluations. The client classification, locale and occasional perspectives are engaged in the recommendation process.

Authors and Affiliations

T. Manjula
PG Scholar, Department of MCA, St. Ann's College of Engineering & Technology, Chirala, Andhra Pradesh, India
P. S. Naveen Kumar
Assistant Professor, Department of MCA, St. Ann's College of Engineering & Technology, Chirala, Andhra Pradesh, India

Service Rating Prediction, Location Based Social Networks, Location Based Rating Prediction, User Behaviors and Service Recommendation Process

  1. Vincent Wenchen Zheng, Yu Zheng, and Qiang Yang. Joint learning user’s activities and profiles from gps data. In Proceedings of the 2009 International Workshop on Location Based Social Networks, pages 17–20. ACM, 2009.
  2. Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. Urban computing: Concepts, methodologies, and applications. ACM Transaction on Intelligent Systems and Technology (ACM TIST), 2014.
  3. Yu Zheng, Yukun Chen, Xing Xie, and Wei-Ying Ma. GeoLife2.0: A Location-Based Social Networking Service. In MDM, 2009c.
  4. Yu Zheng and Xing Xie. Learning location correlation from gps trajectories. In Mobile Data Management (MDM), 2010 Eleventh International Conference on, pages 27–32. IEEE, 2010.
  5. M. Jiang, P. Cui, X. Chen, F. Wang, W. Zhu and S. Yang, "Social recommendation with cross-domain transferable knowledge," IEEE Trans. Knowl. Data Eng., vol. 27, no. 11, pp. 3084–3097, Nov. 2015.
  6. Y. Sun, N. J. Yuan, X. Xie, K. McDonald and R. Zhang, "Collaborative nowcasting for contextual recommendation," in Proc. 25th Int. World Wide Web Conf. (WWW), 2016, pp. 1407–1418.
  7. Y. Zhang, B. Cao and D.-Y. Yeung, "Multi-domain collaborative filtering," in Proc. 26th Conf. Uncertainty Artif. Intell. (UAI), Catalina Island, CA, USA, 2010, pp. 725–732.
  8. H. Gao, J. Tang, X. Hu and H. Liu, "Content-aware point of interest recommendation on location-based social networks," in Proc. 29th AAAI Conf. Artif. Intell. (AAAI), 2015, pp. 1721–1727.
  9. R. M. Bond et al., "A 61-million-person experiment in social influence and political mobilization," Nature, vol. 489, pp. 295–298, Sep. 2012.
  10. Facebook, accessed on 2013. OnlineAvailable:
  11. S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng and H. Zha, "Like like alike: Joint friendship and interest propagation in social networks," in Proc. WWW, 2011, pp. 537–546.
  12. Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver and A. Hanjalic, "CLiMF: Learning to maximize reciprocal rank with collaborative less-is-more filtering," in Proc. ACM RecSys, 2012, pp. 139–146.
  13. X. Yang, H. Steck and Y. Liu, "Circle-based recommendation in online social networks," in Proc. ACM KDD, 2012, pp. 1267–1275.
  14. X. Yang, H. Steck, Y. Guo and Y. Liu, "On top-k recommendation using social networks," in Proc. ACM RecSys, 2012, pp. 67–74.
  15. Xiwang Yang, Chao Liang, Miao Zhao, Hongwei Wang, Hao Ding, Yong Liu, Yang Li and Junlin Zhang, "Collaborative Filtering-Based Recommendation of Online Social Voting", IEEE Transactions On Computational Social Systems, March 2017.

Publication Details

Published in : Volume 3 | Issue 2 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 66-73
Manuscript Number : CSEIT21831439
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

T. Manjula, P. S. Naveen Kumar, "User and Location Based Collaborative Filtering Recommendation in Social Networks", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 2, pp.66-73, January-February-2018.
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