A Novel Approach to Evaluate the Service Quality by Exploring Social User Contextual Information

Authors(3) :-E.Jai Vinitha, J. Maruthu Pandi, R. Divya Bharathi

With the increase of social media and e-commerce, enormous people prefer to share their experience and rate on review websites. Existing research are mainly focused on personalized recommendation and rating prediction but evaluating the quality of service for recommender system is more important. The proposed approach focuses on service quality evaluation. There are some challenges that do not have enough review information for extracting opinion. In this paper, a Service Quality Evaluation model is proposed to evaluate the service quality. The proposed model can be done in three steps. First step is to calculate the entropy which is utilized in users’ confidence value. Second, to explore the contextual features of user rating in which the spatial-temporal features and sentimental features are reviewed. The final step is to fuse the above two steps into a unified model for calculating the overall confidence value to perform service quality evaluation. The experiments are implemented by using Yelp and Douban dataset.

Authors and Affiliations

E.Jai Vinitha
Department of IT, M.Tech, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India
J. Maruthu Pandi
Department of IT, Assistant Professor, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India
R. Divya Bharathi
Department of IT, M.Tech, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India

Spatio-temporal features, sentimental features, Data Mining, Contextual Information of User

  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., vol. 17, no. 6, pp. 734?749, Jun. 2005.
  2. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” in Proc. 10th Int. Conf. World Wide Web, 2001, pp. 285?295.
  3. M. Jahrer, A. Toscher, and R. Legenstein, “Combining predictions for accurate recommender systems,” in Proc. 16th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2010, pp. 693?702.
  4. T. Ma, et al., “Social network and tag sources based augmenting collaborative recommender system,” IEICE Trans. Inf. Syst., vol. E98 D, no. 4, pp. 902?910, 2015.
  5. R. Keshavan, A. Montanari, and S. Oh, “Matrix completion from noisy entries,” J. Mach. Learn. Res., vol. 11, pp. 2057?2078, 2010.
  6. Y. Koren, “Factorization meets the neighborhood: A multifaceted collaborative filtering model,” in Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2008, pp. 426?434.
  7. Y. Koren, “Collaborative filtering with temporal dynamics,” in Proc. 15th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2009, pp. 447?456.
  8. J. Herlocker, J. Konstan, L. Terveen, and J. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Trans. Inf. Syst., vol. 22, no. 1, pp. 5?53, 2004.
  9. N. Liu, M. Zhao, and Q. Yang, “Probabilistic latent preference analysis for collaborative filtering,” in Proc. 18th ACM Conf. Inf. Knowl. Manage., 2009, pp. 759?766.
  10. Z. Fu, K. Ren, J. Shu, X. Sun, and F. Huang, “Enabling personalized search over encrypted outsourced data with efficiency improvement,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 9, pp. 2546?2559, Sep. 2016.
  11. Y. Chen and J. Canny, “Recommending ephemeral items at web scale,” in Proc. 34th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2011, pp. 1013?1022.
  12. M. Harvey, M. Carman, I. Ruthven, and F. Crestani, “Bayesian latent variable models for collaborative item rating prediction,” in Proc. 20th ACM Int. Conf. Inf. Knowl. Manage., 2011, pp. 699?708.
  13. X. Yang, Y. Guo, and Y. Liu, “Bayesian-inference based recommendation in online social networks,” in Proc. IEEE INFOCOM, 2011, pp. 551?555.
  14. H. Ma, D. Zhou, C. Liu, M. Lyu, and I. King, “Recommender systems with social regularization,” in Proc. 4th ACM Int. Conf. Web Search Data Mining, 2011, pp. 287?296.
  15. L. Yu, R. Pan, and Z Li, “Adaptive social similarities for recommender systems,” in Proc. 5th ACM Conf. Recommender Syst., 2011, pp. 257?260.
  16. P. Bedi, H. Kaur, and S. Marwaha, “Trust based recommender system for Semantic Web,” in Proc. 20th Int. Joint Conf. Artifical Intell., 2007, pp. 2677?2682.
  17. P. Cui, F. Wang, S. Liu, M. Ou, S. Yang, and L. Sun, “Who should share what? Item-level social influence prediction for users and posts ranking,” in Proc. 34th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2011, pp. 185?194.
  18. S. Scellato, A. Noulas, and C. Mascolo, “Exploiting place features in link prediction on location-based social networks,” in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2011, pp. 1046?1054.
  19. Z. Wang, L. Sun, W. Zhu, S. Yang, H. Li, and D. Wu, “Joint social and content recommendation for user-generated videos in online social network,” IEEE Trans. Multimedia, vol. 15, no. 3, pp. 698? 709, Apr. 2013.
  20. Y. Chen, A. Cheng, and W. H. Hsu, “Travel recommendation by mining people attributes and travel group types from community- contributed photos,” IEEE Trans. Multimedia, vol. 15, no. 6, pp. 1283?1295, Oct. 2013.
  21. G. Zhao, X. Qian, and X. Xie, “User-service rating prediction by exploring social users’ rating behaviors,” IEEE Trans. Multimedia, vol. 18, no. 3, pp. 496?506, Mar. 2016.
  22. G. Zhao, X. Qian, and C. Kang, “Service rating prediction by exploring social mobile users’ geographic locations,” IEEE Trans. Big Data, to be published. Doi: 10.1109/TBDATA.2016.2552541.
  23. X. Yang, H. Steck, and Y. Liu, “Circle-based recommendation in online social networks,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 1267?1275.
  24. M. Jiang, et al., “Social contextual recommendation,” in Proc. 21st ACM Int. Conf. Inf. Knowl. Manage., 2012, pp. 45?54.
  25. X. Qian, H. Feng, G. Zhao, and T. Mei, “Personalized recommendation combining user interest and social circle,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 7, pp. 1487?1502, Jul. 2014.
  26. H. Feng and X. Qian, “Recommendation via user’s personality and social contextual,” in Proc. 22nd ACM Int. Conf. Inf. Knowl. Manage., 2013, pp. 1521?1524.
  27. X. Lei, X. Qian, and G. Zhao, “Rating prediction based on social sentiment from textual reviews,” IEEE Trans. Multimedia, vol. 18, no. 9, pp. 1910?1921, Sep. 2016.
  28. G. Zhao and X. Qian, “Service objective evaluation via exploring social users’ rating behaviors,” in Proc. IEEE Int. Conf. Multimedia Big Data, 2015, pp. 228?235.
  29. Y. Koren, “Collaborative filtering with temporal dynamics,” in Proc. 15th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2009, pp. 447?456.
  30. G.Dror, N. Koenigstein, and Y. Koren, “Yahoo! Music recommendations: Modeling music ratings with temporal dynamics and item taxonomy,” in Proc. 5th ACM Conf. Recommender Syst., 2011, pp. 165?172.
  31. M. Jamali and M. Ester, “A matrix factorization technique? with trust propagation for recommendation in social networks,” in Proc. 4th ACM Conf. Recommender Syst., 2010, pp. 135?142.
  32. R. Salakhutdinov and A. Mnih, “Probabilistic matrix factorization,” in Proc. NIPS, 2007, pp. 1257?1264.
  33. Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30?37, Aug. 2009.
  34. K. Lee and K. Lee, “Using dynamically promoted experts for music recommendation,” IEEE Trans. Multimedia, vol. 16, no. 5, pp. 1201?1210, Aug. 2014.
  35. X. Yang, T. Zhang, and C. Xu, “Cross-domain feature learning in multimedia,” IEEE Trans. Multimedia, vol. 17, no. 1, pp. 64?78, Jan. 2015.
  36. X. Wang, et al., “Semantic-based location recommendation with multimodal venue semantics,” IEEE Trans. Multimedia, vol. 17, no. 3, pp. 409?419, Mar. 2015.
  37. L. Hu, A. Sun, and Y. Liu, “Your neighbors affect your ratings: On geographical neighborhood influence to rating prediction,” in Proc. 37th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2014, pp. 345?354.
  38. W. Zhang, G. Ding, L. Chen, C. Li, and C. Zhang, “Generating virtual ratings from chinese reviews to augment online recommendations,” ACM Trans. Intell. Syst. Technol., vol. 4, no. 1, 2013, Art. no. 9.
  39. S. Tan, et al., “Interpreting the public sentiment variations on Twitter,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 5, pp. 1158?1170, May 2014.
  40. S. Chelaru, I. Altingovde, S. Siersdorfer, and W. Nejdl, “Analyzing, detecting, and exploiting sentiment in web queries,” ACM Trans. Web, vol. 8, no. 1, 2013, Art. no. 6.
  41. V. Leroy, B. Cambazoglu, and F. Bonchi, “Cold start link prediction,” in Proc. 16th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2010, pp. 393?402.
  42. P. Lou, G. Zhao, X. Qian, H. Wang, and X. Hou, “Schedule a rich sentimental travel via sentimental POI mining and recommendation,” in Proc. IEEE 2nd Int. Conf. Multimedia Big Data, 2016, pp. 33?40.
  43. D. Quercia, N. Lathia, F. Calabrese, G. Lorenzo, and J. Crowcroft, “Recommending social events from mobile phone location data,” in Proc. 2010 IEEE Int. Conf. Data Mining, 2010, pp. 971?976.

Publication Details

Published in : Volume 3 | Issue 5 | May-June 2017
Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 132-138
Manuscript Number : ICASCT2523
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

E.Jai Vinitha, J. Maruthu Pandi, R. Divya Bharathi, " A Novel Approach to Evaluate the Service Quality by Exploring Social User Contextual Information", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 5, pp.132-138, May-June-2017.
Journal URL : https://ijsrst.com/ICASCT2523
Citation Detection and Elimination     |      | | BibTeX | RIS | CSV

Article Preview