CFP last date
20 March 2024
Reseach Article

Social Popularity based SVD++ Recommender System

by Rajeev Kumar, B. K. Verma, Shyam Sunder Rastogi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 14
Year of Publication: 2014
Authors: Rajeev Kumar, B. K. Verma, Shyam Sunder Rastogi
10.5120/15279-4033

Rajeev Kumar, B. K. Verma, Shyam Sunder Rastogi . Social Popularity based SVD++ Recommender System. International Journal of Computer Applications. 87, 14 ( February 2014), 33-37. DOI=10.5120/15279-4033

@article{ 10.5120/15279-4033,
author = { Rajeev Kumar, B. K. Verma, Shyam Sunder Rastogi },
title = { Social Popularity based SVD++ Recommender System },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 14 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number14/15279-4033/ },
doi = { 10.5120/15279-4033 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:56.901213+05:30
%A Rajeev Kumar
%A B. K. Verma
%A Shyam Sunder Rastogi
%T Social Popularity based SVD++ Recommender System
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 14
%P 33-37
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommender systems have shown a lot of awareness in the past decade. Due to their great business value, recommender systems have also been successfully deployed in business, such as product recommendation at flipkart, HomeShop18, and music recommendation at Last. fm, Pandora, and movie recommendation at Flixstreet, MovieLens, and Jinni. In the past few years, the incredible growth of Web 2. 0 web sites and applications constitute new challenges for Traditional recommender systems. Traditional recommender systems always ignore social interaction among users. But in our real life, when we are asking our friends or looking opinions, reviews for recommendations of Mobile or heart touching music, movies, electronic gadgets, restaurant, book, games, software Apps, we are actually using social information for recommendations. In this paper social popularity factor are incorporated in SVD++ factorization method as implicit feedback to improve accuracy and scalability of recommendations.

References
  1. B G. Adomavicius and A. Tuzhilin, "Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions", IEEE Transactions on Knowledge and Data Engineering 17 (2005), 634–749.
  2. Deerwester, S. , Dumais, S. T. , Furnas, G. W. , Landauer, T. K. , and Harshman, R. (1990). Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science. 41(6).
  3. R. Harshman, "Indexing by Latent Semantic Analysis", Journal of the Society for Information Science 41 (1990), 391–407
  4. Sarwar, B. M. , Karypis, G. , Konstan, J. A. , and Riedl, J. (2000). Application of Dimensionality Reduction in Recommender System—A Case Study. In ACM WebKDD'00 (Web-mining for Ecommerce Workshop).
  5. Sarwar, B. M. , Konstan, J. A. , Borchers, A. , and Riedl, J. 1999. "Applying Knowledge from KDD to Recommender Systems. " Technical Report TR99-013, Dept. of Computer Science, University of Minnesota.
  6. R. Bell and Y. Koren, "Lessons from the Netflix Prize Challenge", SIGKDD Explorations 9 (2007), 75–79.
  7. Yehuda Koren, 2008, Factorization meets the Neighborhood: a Multifaceted Collaborative Filtering Model In Proc. Of ACM KDD'08, August 24–27, Las Vegas, Nevada, USA
  8. Bennet and S. Lanning, "The Netflix Prize", KDD Cup and Workshop, 2007. www. netflixprize. com.
  9. J. Canny, "Collaborative Filtering with Privacy via Factor Analysis", Proc. 25th ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIRŠ02), pp. 238–245, 2002
  10. D. Blei, A. Ng, and M. Jordan, "Latent Dirichlet Allocation", Journal of Machine Learning Research 3 (2003), 993–1022
  11. Scott, B. A. , & Judge, T. A. (2009). The popularity contest at work: Who wins, why, and what do they receive? Journal of Applied Psychology, 94(1), 20-33.
  12. Anderson, L. and Holt, C. (1997). Information cascades in the laboratory The American Economic Review, 87, 847-863.
  13. Lansu, T. M. , & Cillessen, A. N. (2012). Peer status in emerging adulthood: Associations of popularity and preference with social roles and behavior. Journal of Adolescent Research, 27(1), 132-150.
  14. Shardanand, U. , and Maes, P. (1995). Social Information Filtering: Algorithms for Automating' Word of Mouth'. In Proc. of CHI '95.
  15. Adamic, L. (2002). Zipf, power-laws, and pareto-a ranking tutorial. Glottometrics, 3, 143-150.
  16. S. Funk, "Netflix Update: Try This At Home", http://sifter. org/˜simon/journal/20061211. html, 2006.
  17. D. Goldberg, D. Nichols, B. M. Oki and D. Terry, "Using Collaborative Filtering to Weave an Information Berry, M. W. , Dumais, S. T. , and O'Brian, G. W. (1995). Using Linear Algebra for Intelligent Information Retrieval. SIAM Review, 37(4).
  18. Simon Dooms, Toon De Pessemier, Dieter Verslype, Jelle Nelis, Jonas De Meulenaere, Wendy Van den Broeck, Luc Martens, and Chris Develder. Omus: an optimized multimedia service for the home environment. Multimedia Tools and Applications , 2013
  19. Jonathan L Herlocker, Joseph A Konstan, Al Borchers, and John Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 230{237. ACM, 1999.
  20. James Bennett and Stan Lanning. The Netflix prize. In Proceedings of KDD cup and workshop , volume 2007, page 35, 2007
  21. Guy Shani and Asela Gunawardana. Evaluating recommendation systems. In Recommender systems handbook, pages 257, Springer, 2011.
  22. Gupta, D. , and Goldberg, K. (1999). Jester 2. 0: A Linear Time Collaborative Filtering Algorithm Applied to Jokes. In Proc. of the ACM SIGIR '99.
  23. Herlocker, J. L. , Konstan, J. A. , Borchers, A. , and Riedl, J. (1999). An Algorithmic Framework for Performing Collaborative Filtering. In Proc. Of ACM SIGIR'99.
  24. Hill, W. , Stead, L. , Rosenstein, M. , and Furnas, G. (1995). Recommending and Evaluating Choices in a Virtual Community of Use. In Proc. of CHI'95.
  25. Simon D. , Toon De P. and Luc M. MovieTweetings: a Movie Rating Dataset Collected From Twitter In Proc. of Crowdrec 2013.
  26. Simon D. , Toon De P. and Luc M. MovieTweetings: a Movie Rating Dataset Collected From Twitter In Proc. of Crowdrec 2013
  27. Hill, W. , Stead, L. , Rosenstein, M. , and Furnas, G. (1995). Recommending and Evaluating Choices in a Virtual Community of Use. In Proc. of CHI'95.
  28. Sharda nand, U. , and Maes, P. (1995). Social Information Filtering: Algorithms for Automating' Word of Mouth'. In Proc. of CHI '95.
  29. Sarwar, B. M. , Karypis, G. , Konstan, J. A. , and Riedl, J. (2000). Analysis of Recommendation Algorithms for E-Commerce. In Proc. of the ACM EC'00 Conference. Minneapolis, MN, pp. 158-167.
  30. Resnick, P. , Iacovou, N. , Suchak, M. , Bergstrom, P. , and Riedl, J. (1994). GroupLens: A Open Architecture for Collaborative Filtering of Netnews. In Proc. of CSCW '94, Chapel Hill, NC.
Index Terms

Computer Science
Information Sciences

Keywords

CF Based Recommendation SVD Social Popularity