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An Improved Collaborative Filtering Algorithm Based on Bhattacharyya Coefficient and LDA Topic Model

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Artificial Intelligence (ICAI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 888))

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Abstract

Collaborative filtering (CF) is the most successful method used in designing recommendation systems, which includes the neighbor-based method and the model-based method. Traditional neighbor-based method calculates similarity only based on the rating matrix, but the rating matrix is very sparse. Therefore, to address the problem of sparsity, we proposed an improved collaborative filtering algorithm unified Bhattacharyya coefficient and LDA topic model (UBL-CF). UBL-CF utilized the LDA topic model to mine potential topic information in the tag set and embed the underlying topic information into the progress of the calculation of similarity. Meanwhile, it introduces Bhattacharyya coefficient to alleviate the data sparsity without common ratings. Experimental results show that our method has better prediction in accuracy.

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References

  1. Mennel, P.A., Melgoza, P., Gyeszly, S.D.: Information overload. Collect. Build. 21(21), 32–43 (2002)

    Google Scholar 

  2. Anidorifón, L., Santosgago, J., Caeirorodríguez, M., et al.: Recommender systems. Commun. ACM 40(3), 56–58 (2015)

    Google Scholar 

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

    Article  Google Scholar 

  4. Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_10

    Chapter  Google Scholar 

  5. Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Eighteenth National Conference on Artificial Intelligence, pp. 187–192. American Association for Artificial Intelligence (2002)

    Google Scholar 

  6. Resnick, P., Iacovou, N., Suchak, M., et al.: GroupLens: an open architecture for collaborative filtering of netnews. In: ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM (1994)

    Google Scholar 

  7. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  8. Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 123–129. AAAI Press (2015)

    Google Scholar 

  9. Liu, H., Hu, Z., Mian, A., et al.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl. Based Syst. 56(3), 156–166 (2014)

    Article  Google Scholar 

  10. Aggarwal, C.C.: Knowledge-based recommender systems. In: Aggarwal, C.C. (ed.) Recommender Systems, pp. 167–197. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3_5

    Chapter  Google Scholar 

  11. Felfernig, A., Burke, R.: Constraint-based recommender systems: technologies and research issues. In: International Conference on Electronic Commerce, p. 3. ACM (2008)

    Google Scholar 

  12. Ji, H., Li, J., Ren, C., et al.: Hybrid collaborative filtering model for improved recommendation. In: IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 142–145. IEEE (2013)

    Google Scholar 

  13. Lucas, J.P., Luz, N., Anacleto, R., et al.: A hybrid recommendation approach for a tourism system. Expert Syst. Appl. 40(9), 3532–3550 (2013)

    Article  Google Scholar 

  14. Ji, K., Sun, R., Li, X., et al.: Improving matrix approximation for recommendation via a clustering-based reconstructive method. Neurocomputing 173(P3), 912–920 (2016)

    Article  Google Scholar 

  15. Barua, S., Gao, X., Pasman, H., et al.: Bayesian network based dynamic operational risk assessment. J. Loss Prev. Process Ind. 41, 399–410 (2016)

    Article  Google Scholar 

  16. Xuan, N.L., Vu, T., Le, T.D., et al.: Addressing cold-start problem in recommendation systems. In: International Conference on Ubiquitous Information Management and Communication, pp. 208–211. ACM (2008)

    Google Scholar 

  17. Almazro, D., Shahatah, G., Albdulkarim, L., et al.: A survey paper on recommender systems. Comput. Sci. (2010)

    Google Scholar 

  18. Sun, D., Li, C., Luo, Z.: A content-enhanced approach for cold-start problem in collaborative filtering. In: International Conference on Artificial Intelligence, Management Science and Electronic Commerce, pp. 4501–4504. IEEE (2011)

    Google Scholar 

  19. Wang, W., Zhang, D., Zhou, J.: COBA: a credible and co-clustering filterbot for cold-start recommendations. In: Wang, Y., Li, T. (eds.) Practical Applications of Intelligent Systems. Advances in Intelligent and Soft Computing, vol. 124, pp. 467–476. Springer, Cham (2011). https://doi.org/10.1007/978-3-642-25658-5_56

    Chapter  Google Scholar 

  20. Ge, S., Ge, X.: An SVD-based collaborative filtering approach to alleviate cold-start problems. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1474–1477. IEEE (2012)

    Google Scholar 

  21. Wang, Z., Wang, Y., Wu, H.: Tags meet ratings: improving collaborative filtering with tag-based neighborhood method. In: The Workshop on Social Recommender Systems. IUI (2010)

    Google Scholar 

  22. Ji, K., Shen, H.: Addressing cold-start: scalable recommendation with tags and keywords. Knowl. Based Syst. 83(1), 42–50 (2015)

    Article  Google Scholar 

  23. Na, G., Yang, M.: Topic model embedded in collaborative filtering recommendation algorithm. Comput. Sci. 43(3), 57–61 (2016)

    MathSciNet  Google Scholar 

  24. Zhao, X., Niu, Z., Chen, W., et al.: A hybrid approach of topic model and matrix factorization based on two-step recommendation framework. J. Intell. Inf. Syst. 44(3), 335–353 (2015)

    Article  Google Scholar 

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Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant 61432008, 61272222.

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210023, China

    Chunxia Zhang & Ming Yang

Authors
  1. Chunxia Zhang
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  2. Ming Yang
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Corresponding author

Correspondence to Ming Yang .

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Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  2. Hong Kong University of Science and Technology, Hong Kong SAR, China

    Qiang Yang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. JD Finance, Beijing, China

    Yu Zheng

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Zhang, C., Yang, M. (2018). An Improved Collaborative Filtering Algorithm Based on Bhattacharyya Coefficient and LDA Topic Model. In: Zhou, ZH., Yang, Q., Gao, Y., Zheng, Y. (eds) Artificial Intelligence. ICAI 2018. Communications in Computer and Information Science, vol 888. Springer, Singapore. https://doi.org/10.1007/978-981-13-2122-1_17

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  • DOI: https://doi.org/10.1007/978-981-13-2122-1_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2121-4

  • Online ISBN: 978-981-13-2122-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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