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Integrating with Social Network to Enhance Recommender System Based-on Dempster-Shafer Theory

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Computational Social Networks (CSoNet 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9795))

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Abstract

In this paper, we developed a new collaborative filtering recommender system integrating with a social network that contains all users. In this system, user preferences and community preferences extracted from the social network are modeled as mass functions, and Dempster’s rule of combination is selected for fusing the preferences. Especially, with the community preferences, both the sparsity and cold-start problems are completely eliminated. So as to evaluate and demonstrate the advantage of the new system, we have conducted a range of experiments using Flixster data set.

This research work was supported by JSPS KAKENHI Grant No. 25240049.

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References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  2. Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)

    Article  Google Scholar 

  3. Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, New York (2011)

    Chapter  Google Scholar 

  4. Al-hassan, M., Lu, H., Lu, J.: A semantic enhanced hybrid recommendation approach: a case study of e-government tourism service recommendation system. Decis. Support Syst. 72, 97–109 (2015)

    Article  Google Scholar 

  5. Shambour, Q., Lu, J.: A hybrid trust-enhanced collaborative filtering recommendation approach for personalized government-to-business e-services. Int. J. Intell. Syst. 26(9), 814–843 (2011)

    Article  Google Scholar 

  6. Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Discov. 5(1/2), 115–153 (2001)

    Article  MATH  Google Scholar 

  7. Jannach, D., Zanker, M., Ge, M., Gröning, M.: Recommender systems in computer science and information systems – a landscape of research. In: Huemer, C., Lops, P. (eds.) EC-Web 2012. LNBIP, vol. 123, pp. 76–87. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  8. Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22(1), 116–142 (2004)

    Article  Google Scholar 

  9. Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)

    Article  Google Scholar 

  10. Lucas, J.P., Luz, N., García, M.N.M., Anacleto, R., de Almeida Figueiredo, A.M., Martins, C.: A hybrid recommendation approach for a tourism system. Expert Syst. Appl. 40(9), 3532–3550 (2013)

    Article  Google Scholar 

  11. Shambour, Q., Lu, J.: An effective recommender system by unifying user and item trust information for B2B applications. J. Comput. Syst. Sci. 81(7), 1110–1126 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  12. Grčar, M., Mladenič, D., Fortuna, B., Grobelnik, M.: Data sparsity issues in the collaborative filtering framework. In: Nasraoui, O., Zaïane, O.R., Spiliopoulou, M., Mobasher, B., Masand, B., Yu, P.S. (eds.) WebKDD 2005. LNCS (LNAI), vol. 4198, pp. 58–76. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. Nguyen, V.D., Huynh, V.N.: A reliably weighted collaborative filtering system. In: Destercke, S., Denoeux, T. (eds.) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. LNCS, vol. 9161, pp. 429–439. Springer, Switzerland (2015)

    Chapter  Google Scholar 

  14. Wickramarathne, T.L., Premaratne, K., Kubat, M., Jayaweera, D.T.: Cofids: a belief-theoretic approach for automated collaborative filtering. IEEE Trans. Knowl. Data Eng. 23(2), 175–189 (2011)

    Article  Google Scholar 

  15. He, J., Chu, W.W.: A social network-based recommender system (SNRS). In: Memon, N., Xu, J.J., Hicks, D.L., Chen, H. (eds.) Data Mining for Social Network Data. Annals of Information Systems, vol. 12, pp. 47–74. Springer, US (2010)

    Chapter  Google Scholar 

  16. Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: SIGIR, pp. 195–202 (2009)

    Google Scholar 

  17. Sun, Z., Han, L., Huang, W., Wang, X., Zeng, X., Wang, M., Yan, H.: Recommender systems based on social networks. J. Syst. Softw. 99, 109–119 (2015)

    Article  Google Scholar 

  18. Guo, G., Zhang, J., Thalmann, D.: Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowl. Based Syst. 57, 57–68 (2014)

    Article  Google Scholar 

  19. Papagelis, M., Plexousakis, D., Kutsuras, T.: Alleviating the sparsity problem of collaborative filtering using trust inferences. In: Herrmann, P., Issarny, V., Shiu, S.C.K. (eds.) iTrust 2005. LNCS, vol. 3477, pp. 224–239. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  20. Wu, H., Yue, K., Pei, Y., Li, B., Zhao, Y., Dong, F.: Collaborative topic regression with social trust ensemble for recommendation in social media systems. Knowl. Based Syst. 97, 111–122 (2016)

    Article  Google Scholar 

  21. Nguyen, V.-D., Huynh, V.-N.: A community-based collaborative filtering system dealing with sparsity problem and data imperfections. In: Pham, D.-N., Park, S.-B. (eds.) PRICAI 2014. LNCS, vol. 8862, pp. 884–890. Springer, Heidelberg (2014)

    Google Scholar 

  22. Tang, L., Liu, H.: Community Detection and Mining in Social Media. Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool Publishers, San Rafael (2010)

    Google Scholar 

  23. Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38, 325–339 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  24. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  25. Smets, P.: Practical uses of belief functions. In: UAI 1999, pp. 612–621. Morgan Kaufmann Publishers Inc., San Mateo (1999)

    Google Scholar 

  26. Gregory, S.: A fast algorithm to find overlapping communities in networks. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 408–423. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  27. Xie, J., Szymanski, B.K.: Towards linear time overlapping community detection in social networks. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part II. LNCS, vol. 7302, pp. 25–36. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  28. Kim, J., Lee, J.: Community detection in multi-layer graphs: a survey. ACM SIGMOD Rec. 44(3), 37–48 (2015)

    Article  Google Scholar 

  29. Sun, P.G., Gao, L.: A framework of mapping undirected to directed graphs for community detection. Inf. Sci. 298, 330–343 (2015)

    Article  Google Scholar 

  30. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR 1999, pp. 230–237. ACM (1999)

    Google Scholar 

  31. Bloch, I.: Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recogn. Lett. 17(8), 905–919 (1996)

    Article  Google Scholar 

  32. Hewawasam, K.K.R., Premaratne, K., Shyu, M.: Rule mining and classification in a situation assessment application: A belief-theoretic approach for handling data imperfections. IEEE Trans. Syst., Man, Cybern., Syst., Part B, 37(6), 1446–1459 (2007)

    Google Scholar 

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Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan

    Van-Doan Nguyen & Van-Nam Huynh

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  1. Van-Doan Nguyen
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  2. Van-Nam Huynh
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Correspondence to Van-Doan Nguyen .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University , Ho Chi Minh City, Vietnam

    Hien T. Nguyen

  2. VSB-Technical University of Ostrava , Ostrava, Poland

    Vaclav Snasel

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Nguyen, VD., Huynh, VN. (2016). Integrating with Social Network to Enhance Recommender System Based-on Dempster-Shafer Theory. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-42345-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42344-9

  • Online ISBN: 978-3-319-42345-6

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