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A Study of Recommender Systems on Social Networks and Content-based Web Systems

by Rahul Hooda, Kulvinder Singh, Sanjeev Dhawan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 97 - Number 4
Year of Publication: 2014
Authors: Rahul Hooda, Kulvinder Singh, Sanjeev Dhawan
10.5120/16996-7128

Rahul Hooda, Kulvinder Singh, Sanjeev Dhawan . A Study of Recommender Systems on Social Networks and Content-based Web Systems. International Journal of Computer Applications. 97, 4 ( July 2014), 23-28. DOI=10.5120/16996-7128

@article{ 10.5120/16996-7128,
author = { Rahul Hooda, Kulvinder Singh, Sanjeev Dhawan },
title = { A Study of Recommender Systems on Social Networks and Content-based Web Systems },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 97 },
number = { 4 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume97/number4/16996-7128/ },
doi = { 10.5120/16996-7128 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:23:14.036524+05:30
%A Rahul Hooda
%A Kulvinder Singh
%A Sanjeev Dhawan
%T A Study of Recommender Systems on Social Networks and Content-based Web Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 97
%N 4
%P 23-28
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Everybody rely on recommendations in everyday life from other people either orally or by reviews printed in newspapers or websites. Recommender systems are a subfamily of information filtering systems that explore to predict the 'rating' or 'preference' that user would give to an item. These systems are best known for their use in e-commerce websites where they use input about a customer's interest to generate a list of recommended items. Many recommender systems explicitly rate to represent customer's interest by using only the items that the customers purchase, but can also use other attributes, including items viewed, subject interests and demographic data. They direct users towards those items that meet their needs by reducing unwanted information spaces. To perform recommendation a number of techniques have been proposed, including content-based, collaborative, and hybrid techniques. To improve performance and to outweigh the drawbacks of individual recommendation techniques, these techniques are sometimes combined to form hybrid recommenders. This paper is categorized into seven sections. Section-I presents the introduction related to the recommendation systems used in the social networks and on-line Web systems, section-II critically analyzed the related literature work about collaborative recommendation, content-based recommendation, and hybrid recommendation, section-III describes the business aspects of recommender systems, section-IV describes various ways of displaying recommendations to a customer, section-V investigates the various recommendations techniques and their limitations and section-VI provides the conclusion of the recommender systems. In this paper the efforts are made to review and discover the techniques to investigate the proper usage of recommender systems in the e-commerce applications.

References
  1. Rich, E. User Modeling via Stereotypes. Cognitive Science, vol. 3, no. 4, pp. 329-354, 1979.
  2. Goldberg, D. , D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, vol. 35, no. 12, pp. 61-70, 1992.
  3. Konstan, J. A. , B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, vol. 40, no. 3, pp. 77-87, 1997.
  4. Resnick, P. , N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM conference on Computer Supported Cooperative Work, pp. 175- 186, New York, NY, USA, 1994.
  5. Hill, W. , L. Stead, M. Rosenstein, and G. Furnas. Recommending and evaluating choices in a virtual community of use. In Proceedings of CHI'95 SIGCHI Conference on Human Factors in Computing Systems, pp. 194-201, ACM press, New York, NY, USA, 1995.
  6. Shardanand and P. Maes. Social information filtering: Algorithms for automating 'word of mouth'. In Proceedings of SIGCHI Conference on Human Factors in Computing Systems, pp. 210-217, ACM press, NY, USA, 1995.
  7. Goldberg, K. , T. Roeder, D. Gupta and C. Perkins. Eigen taste: A constant time collaborative filtering algorithm. Information Retrieval Journal, vol. 4, no. 2, pp. 133-151, July 2001.
  8. Breese, J. S. , D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43- 52, Morgan Kaufmann Publishers, San Francisco, CA, USA, 1998.
  9. Delgado, J. and N. Ishii. Memory-based weighted-majority prediction for recommender systems. In ACM SIGIR'99 Workshop on Recommender Systems: Algorithms and Evaluation, ACM press, New York, 1999.
  10. Nakamura, A. and N. Abe. Collaborative filtering using weighted majority prediction algorithms. In Proceedings of the 15th International Conference on Machine Learning (ICML'98), pp. 395- 403, Morgan Kaufmann Publishers,CA, USA, 1998.
  11. Hofmann, T. Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis. In Proc. of the 26th Annual International ACM SIGIR Conference, pp. 259-266, ACM press, New York, NY, USA, 2003.
  12. Pavlov, D. and D. Pennock. A Maximum Entropy Approach To Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains. In Proceedings of the 16th Annual Conference on Neural Information Processing Systems (NIPS'02), pp. 1441-1448, MIT Press, Cambridge, Mass, USA, 2002.
  13. Ungar, L. H. , and D. P. Foster. Clustering methods for collaborative filtering. In Proceedings of the workshop on Recommender Systems, pp. 112-125, AAAI Press, Madison, Wisconsin, USA, 1998.
  14. Billsus, D. and M. Pazzani. Learning collaborative information filters. In International Conference on Machine Learning (ICML'98), pp. 46-54, Morgan Kaufmann Publishers, San Francisco, CA, USA, 1998.
  15. Dempster, A. P. , N. Laird and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm, Journal of Royal Statistical Society, Series B, vol. 39, no. 1, pp. 1- 38, 1977.
  16. Salton, G. Automatic Text Processing. Addison-Wesley Longman Publishing co. , Boston, MA, USA, 1989.
  17. Balabanovic, M. and Y. Shoham. Fab: Content-based, collaborative recommendation. Communications of the ACM, vol. 40, no. 3, pp. 66-72, 1997.
  18. Pazzani, M. and D. Billsus. Learning and revising user profiles: The identification of interesting web sites. Machine learning, vol. 27, no. 3, pp. 313-331, 1997.
  19. Billsus, D. & M. Pazzani. A Hybrid User Model for News Story Classification. In Proceedings of 7th International Conference on User Modelling, pp. 99-108, Banff, Canada, 1999.
  20. Billsus, D. and M. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, vol. 10, no. 2-3, pp. 147-180, Kluwer Academic Publishers Hingham, MA, USA, 2000.
  21. Tran, T. and R. Cohen. Hybrid Recommender Systems for Electronic Commerce. In Knowledge-Based Electronic Markets, Papers from AAAI Workshop, Technical Report WS-00-04, pp. 78-83, AAAI Press, 2000.
  22. Pazzani, M. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, vol. 13. No. 5-6, pp. 393-408, December 1999.
  23. Basu, C. , H. Hirsh, and W. Cohen. Recommendation as classification: Using social and content-based information in recommendation. In Proceesdings of the 15th National Conference on Artificial Intelligence (AAAI'98), pp. 714- 720, Madison, Wis, USA, July 1998.
  24. Popescul, A. , L. H. Ungar, D. M. Pennock, and S. Lawrence. Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. In Proceedings of the 17th Conf. on Uncertainty in Artificial Intelligence (UAI'01), pp. 437- 444, Seattle, WA, 2001.
  25. Schein, A. I. , A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and Metrics for cold-start recommendations. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research andDevelopment in Information Retrieval (SIGIR '02), pp. 253-260, ACM press, New York, NY, USA, 2002.
  26. Melville, P. , R. J. Mooney, and R. Nagarajan. Content-Boosted Collaborative Filtering for Improved Recommendations. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, Edmonton, pp. 187-192, AAAI press, Menlo Park, CA, USA, 2002.
  27. Soboroff, I. and C. Nicholas. Combining content and collaboration in text filtering. In 43 IJCAI'99 Workshop: Machine Learning for Information Filtering, Stockholm, Sweden, August 1999.
  28. Schafer, J. A. Konstan & J. Riedl. E-Commerce Recommendation Applications. Data Mining & Knowledge Discovery, vol. 5, no. 1-2, pp. 115-152, 2001.
  29. Forte, D. Shoppers want email with personalized product recommendations, www. listrak. com/about/news-events/shoppers-want-more-emails-with-personalized-product-recommendations, January 15, 2014 [viewed on April 2, 2014].
  30. Myles Anderson. Local Consumer Survey, http://www. brightlocal. com/2010/11/29/local-consumer-review-survey-2010-part-1/, November 29, 2010 [viewed on April 2, 2014].
Index Terms

Computer Science
Information Sciences

Keywords

Electronic commerce recommender system collaborative recommendation content-based recommendation hybrid recommendation

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