A SURVEY OF RECOMMENDER SYSTEM TYPES AND ITS CLASSIFICATION

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Published: Dec 20, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i9.5017
Keywords:
Recommender Systems, Survey, Collaborative filtering, Content based Filtering, Hybrid Filtering

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Akhil P V
Cochin University of Science and Technology
Dr. Shelbi Joseph
Cochin University of Science and Technology

Abstract

The current generation is finding it difficult to find the right information from the enormous amount of data they are presented with in the online platforms. It is hard to spent time online searching for information in such a scenario and it craves for the need of an information filtering system that could help them discover the information they seek. A research field that does this has emerged in the last few years called as recommender systems. A lot of extensive research is happening in the field which is trying to incorporate more attributes to give more precise and relevant personalised recommendations to a user. This paper is focused on reviewing some significant works in the three basic recommender system types including collaborative filtering, content based filtering and hybrid filtering. The paper also have identified and listed the major challenges faced by recommender systems. The main contribution of the paper is in proposing a novel hybrid recommender system which addresses the sparsity and serendipity drawback of recommender systems. The proposed method is expected to deliver more accurate, relevant and novel predictions.

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Vol. 8 No. 9 (2017): NOVEMBER-DECEMBER 2017
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Articles

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Author Biographies

Akhil P V, Cochin University of Science and Technology

School of engineering, Research Scholar

Dr. Shelbi Joseph, Cochin University of Science and Technology

School of Engineering, Assistant Professor

References

J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérrez, Recommender systems survey, Knowledge-Based Systems, 46 (2013) 109-132.

Deshpande, M., and Karypis, G. Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22, 1 (2004), 143-177.

M. Pazzani, A framework for collaborative, content-based, and demographic filtering, Artificial Intelligence Review-Special Issue on Data Mining on the Internet 13 (5-6) (1999) 393–408.

R. Burke, Hybrid recommender systems: survey and experiments, User Modeling and User-Adapted Interaction 12 (4) (2002) 331–370.

Pan C, Li W. Research paper recommendation with topic analysis. In Computer Design and Applications IEEE 2010;4, pp. V4-264.

J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérrez, Recommender systems survey, Knowledge based systems, Vol. 43, 2013, pp 109-132.

M. Balabanovic and Y. Shoham, “Fab: Content-Based, Collaborative Recommendation,†Comm. ACM, vol. 40, no. 3, pp. 66-72, 1997.

Burke R. Hybrid recommender systems: survey and experiments. User Model User-adapted Interact 2002;12(4):331–70.

Breese J, Heckerma D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th conference on uncertainty in artificial intelligence (UAI-98); 1998. p. 43–52.

Sarwar, B., Karypis, G., Konstan, J., and Reidl, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international Conference on World Wide Web (Hong Kong, Hong Kong, May 01 - 05, 2001). ACM, New York, NY, 285-295. DOI= http://doi.acm.org/10.1145/ 371920.372071

Xiaoyuan Su, Taghi M. Khoshgoftaar, A survey of collaborative filtering techniques, Advances in Artificial Intelligence archive, 2009.

Aggarwal, Charu C. (2016). Recommender Systems: The Textbook. Springer. ISBN 9783319296579.

Peter Brusilovsky (2007). The Adaptive Web. p. 325. ISBN 978-3-540-72078-2.

Resnick, P., Iakovou, N., Sushak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. Computer Supported Cooperative Work Conf.

Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating ’Word of Mouth’. Human Factors in Computing Systems Conf.

Frias-Martinez, E., Chen, S. Y., & Liu, X. (2009). Evaluation of a personalized digital library based on cognitive styles: Adaptivity vs. adaptability. International Journal of Information Management, 29, 48–56.

Frias-Martinez, E., Magoulas, G., Chen, S. Y., & Macredie, R. (2006). Automated user modeling for personalized digital libraries. International Journal of Information Management, 26, 234–248.

Kim, J. K., Kim, H. K., Oh, H. Y., & Ryu, Y. U. (2010). A group recommendation system for online communities. International Journal of Information Management, 30, 212–219.

B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, “Item based collaborative filtering recommendation algorithms,†in Proceedings of the 10th International Conference on World Wide Web (WWW ’01), pp. 285–295, May 2001.

Guangping Zhuo, Jingyu Sun and Xueli Yu “A Framework for Multi-Type Recommendationsâ€, Eighth International Conference on Fuzzy Systems and Knowledge Discovery, 2007.

John S., Breese, David Heckerman and Carl Kadie. 1998. (July), Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence.

Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. 1997. GroupLens: applying collaborative filtering to Usenet news. Commun. ACM 40, 3 (Mar. 1997), 77-87.

Yechun Jiang, Jianxun Liu, Mingdong Tang and Xiaoqing (Frank) Liu “An Effective Web Service Recommendation Method based on Personalized Collaborative Filteringâ€, 2011 IEEE International Conference on Web Services.

Qian Wang, Xianhu Yuan, Min Sun “Collaborative Filtering Recommendation Algorithm based on Hybrid User Modelâ€, FSKD, 2010.

Chuangguang Huang and Jian Yin “Effective Association Clusters Filtering to Cold-Start Recommendationsâ€, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

Mustansar Ali Ghazanfar and Adam Prugel-Bennett,†A Scalable, Accurate Hybrid Recommender Systemâ€, 2010 Third International Conference on Knowledge Discovery and Data Mining.

Liang He and Faqing Wu, “A Time-context-based Collaborative Filtering Algorithmâ€, IEEE International Conference on Granular Computing, 2009

Ibrahim A. Almosallam and Yi Shang “A New Adaptive Framework for Collaborative Filtering Predictionâ€, 2008 IEEE Congress on Evolutionary Computation (CEC 2008).

R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison-Wesley, 1999.

G. Salton, Automatic Text Processing. Addison-Wesley, 1989.

N. Belkin and B. Croft, “Information Filtering and Information Retrieval,†Comm. ACM, vol. 35, no. 12, pp. 29-37, 1992.

James Bennett; Stan Lanning (August 12, 2007). "The Netflix Prize". Proceedings of KDD Cup and Workshop 2007.

M. Balabanovic and Y. Shoham, “Fab: Content-Based, Collaborative Recommendation,†Comm. ACM, vol. 40, no. 3, pp. 66-72, 1997.

M. Pazzani and D. Billsus, “Learning and Revising User Profiles: The Identification of Interesting Web Sites,†Machine Learning, vol. 27, pp. 313-331, 1997.

G. Salton, Automatic Text Processing. Addison-Wesley, 1989.

J.J. Rocchio, “Relevance Feedback in Information Retrieval,†SMART Retrieval System—Experiments in Automatic Document Processing, G. Salton, ed., chapter 14, Prentice Hall, 1971.

N. Littlestone and M. Warmuth, “The Weighted Majority Algorithm,†Information and Computation, vol. 108, no. 2, pp. 212- 261, 1994.

G. Somlo and A. Howe, “Adaptive Lightweight Text Filtering,†Proc. Fourth Int’l Symp. Intelligent Data Analysis, 2001.

Y. Zhang, J. Callan, and T. Minka, “Novelty and Redundancy Detection in Adaptive Filtering,†Proc. 25th Ann. Int’l ACM SIGIR Conf., pp. 81-88, 2002.

S. Robertson and S. Walker, “Threshold Setting in Adaptive Filtering,†J. Documentation, vol. 56, pp. 312-331, 2000.

M.G. Vozalis, K.G. Margaritis, Using SVD and demographic data for the enhancement of generalized collaborative filtering, Information Sciences 177 (2007) 3017–3037.

A.B. Barragáns-Martı´nez, E. Costa-Montenegro, J.C. Burguillo, M. Rey-López,

F.A. Mikic-Fonte, A. Peleteiro, A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition, Information Sciences 180 (22) (2010) 4290– 4311.

M.Y.H. Al-Shamri, K.K. Bharadwaj, Fuzzy-genetic approach to recommender systems based on a novel hybrid user model, Expert Systems with Applications 35 (3) (2008) 1386–1399.

M. Lee, Y. Woo, A hybrid recommender system combining collaborative filtering with neural network, Lecture Notes on Computer Sciences 2347 (2002) 531–534.

L.M. Campos, J.M. Fernández-Luna, J.F. Huete, M.A. Rueda-Morales, Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian Networks, International Journal of Approximate Reasoning 51 (7) (2010) 785–799.

S.K. Shinde, U. Kulkami, Hybrid personalizad recommender system using centering–bunching based clustering algorithm, Expert Systems with Applications 39 (1) (2012) 1381–1387.

M. Saranya, T. Atsuhiro, Hybrid recommender systems using latent features, in: Proceedings of the International Conference on Advanced Information Networking and Applications Workshops, 2009, pp. 661–666.

R. Burke, Hybrid web recommender systems, in: P. Brusilovsky, A. Kobsa,W. Nejdl (Eds.), The Adaptive Web, Springer-Verlag, Berlin Heidelberg 2007, pp. 377–408.

S.T. Park, W. Chu, Pairwise preference regression for cold-start recommendation, in: Proceedings of the 2009 ACM Conference on Recommender Systems, 2009, pp. 21–28.

Y.J. Park, A. Tuzhilin, The long tail of recommender systems and how to leverage it, in: Proceedings of the 2008 ACM Conference on Recommender Systems, 2008, pp. 11–18.

A.M. Rashid, G. Karypis, J. Riedl, Learning preferences of new users in recommender systems: an information theoretic approach, in: ACM SIGKDD Explorations Newsletter, vol. 10, issue 2, 2008, pp. 90–100.

P.B. Ryan, D. Bridge, Collaborative recommending using formal concept analysis, Knowledge Based Systems 19 (5) (2006) 309–315.

M. Balabanovic and Y. Shoham, “Fab: Content-Based, Collaborative Recommendation,†Comm. ACM, vol. 40, no. 3, pp. 66-72, 1997.

M. Pazzani, “A Framework for Collaborative, Content-Based, and Demographic Filtering, Artificial Intelligence Rev., pp. 393-408, Dec. 1999.

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Application of Dimensionality Reduction in Recommender Systems—A Case Study,†Proc. ACM WebKDD Workshop, 2000.

A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web, pages 271–280. ACM, 2007.

R. Gemulla, E. Nijkamp, P. J. Haas, and Y. Sismanis. Large-scale matrix factorization with distributed stochastic gradient descent. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 69–77. ACM, 2011

P. Adamopoulos, A. Tuzhilin. On unexpectedness in recommender systems: or how to better expect the unexpected ACM Trans. Intell. Syst. Technol., 5 (4) (2014), pp. 1-32

Y.C. Zhang, D.O. Séaghdha, D. Quercia, T. Jambor Auralist: introducing serendipity into music recommendation Proceedings of the 5th ACM International Conference on Web Search and Data Mining, ACM, New York, NY, USA (2012), pp. 13-22