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Recommender Frameworks Outline System Design and Strategies: A Review

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Knowledge Computing and its Applications

Abstract

Nowadays, right information and service access are the big challenge in the World Wide Web. There are number of tools available to access the right information in the market. Recommender system is the most valuable tool to provide such service. The applications of recommender systems include recommending movies, music, television programs, books, documents, Web sites, conferences, tourism scenic spots and learning materials, and involve the areas of e-commerce, e-learning, e-library, e-government, and e-business services. These recommender systems can be designed with different objectives, strategies, algorithms, and methods. This article discusses in detail about what is recommender system, needs, benefits, challenges, strategies, algorithms, and measures used for designing the recommender system. It also gives the details about the user personalization and customization.

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References

  1. Breese, J. S., Heckerman, D., & Kadie, C. (1998, May). Empirical Analysis of Predictive Algorithms for Collaborative Filtering (Technical Report, Microsoft Research, WA 98052).

    Google Scholar 

  2. Mishra, R., Kumar, P., & Bhasker, B. (2015). A web recommendation system considering sequential information. Decision Support Systems, 75, 1–10.

    Article  Google Scholar 

  3. Barragáns-Martínez, B., Costa-Montenegro, E., & Juncal-Martínez, J. (2015). Developing a recommender system in a consumer electronic device. Expert Systems with Applications, 42, 4216–4228.

    Article  Google Scholar 

  4. Liu, Y., & Yang, J. (2015). Improving ranking-based recommendation by social information and negative similarity. Procedia Computer Science, 55, 732–740.

    Article  Google Scholar 

  5. Chen, H.-H., Ororbia, I. I., Alexander, G., & Giles, C. L. (2008) ExpertSeer: A keyphrase based expert recommender for digital libraries. http://expertseer.ist.psu.edu/.

  6. Felfernig, A., Isak, K., Szabo, K., & Zachar, P. (2012). The VITA financial services sales support environment. www.aaai.org.

  7. Takacs, G. (2009). Scalable collaborative filtering approaches for large recommender systems. Journal of Machine Learning Research, 10, 623–656.

    Google Scholar 

  8. Melville, P., & Sindhwani, V. (2011). Recommender systems. In C. Sammut & G. Webb (Eds.), Encyclopedia of machine learning. Berlin: Springer.

    Google Scholar 

  9. Lee, J., Lee, K., Kim, J. G., & Kim, S. (2015). Personalized academic paper recommendation system. In SRS’15, August 10, 2015, Sydney, NSW, Australia.

    Google Scholar 

  10. Rubens, N., Elahi, M., Sugiyama, M., & Kaplan, D. (2015). Active learning in recommender systems. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook. Boston: Springer. https://doi.org/10.1007/978-1-4899-7637-6_24.

  11. Beel, J., Gipp, B., Langer, S., & Breitinger, C. (2015). Research paper recommender systems: A literature survey. International Journal on Digital Libraries, 1–34. https://doi.org/10.1007/s00799-15-0156-0.

  12. Beel, J., Genzmehr, M., Langer, S., Nürnberger, A., & Gipp, B. (2013). A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation. In Repsys’13, October 12, 2013, Hong Kong China. ACM. ISBN: 978-1-4503-2465-6/13/10.

    Google Scholar 

  13. Song, Y., Dixon, S., & Pearce, M. (2012). A survey of music recommendation systems and future perspectives. In 9th International Symposium on Computer Music Modelling and Retrieval (CMMR 2012), June 19–22, 2012, Queen Mary University of London.

    Google Scholar 

  14. Adomavicius, G., & Tuzhilin, A. (2005). Toward 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(6), 734–749.

    Article  Google Scholar 

  15. Ricci, F., Rokach, L., & Shapira, B. (2015). Introduction to recommender systems handbook. In F. Ricci, L. Rokach, B. Shapira, & P. Kantor (Eds.), Recommender systems handbook. Berlin: Springer. https://doi.org/10.1007/978-1-4899-7637-6_24.

  16. Ge, Y., Xiong, H., Tuzhilin, A., Xiao, K., Gruteser, M., & Pazzani, M. (2010). An energy-efficient mobile recommender system. In KDD’10, July 25–28, 2010, Washington, DC, USA. ACM. ISBN: 978-1-4503-0055-110/07.

    Google Scholar 

  17. Rennie, J. D. M., & Srebro, N. (2005). Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.

    Google Scholar 

  18. Puglisi, S., Parra-Arnau, J., Forné, J., & Rebollo-Monedero, D. (2015). On content-based recommendation and user privacy in social-tagging systems. Computer Standards & Interfaces, 41, 17–27.

    Article  Google Scholar 

  19. Melville, P., Mooney, R. J., & Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002) (pp. 187-192), Edmonton, Canada, July 2002.

    Google Scholar 

  20. Bobadilla, J. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132.

    Article  Google Scholar 

  21. Ricci, F. (2002, November/December). Travel recommender systems. IEEE Intelligent System, 17, 54–57.

    Google Scholar 

  22. Burke, R. (2007). Hybrid web recommender systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web (pp. 377–408)., LNCS 4321 Berlin: Springer.

    Chapter  Google Scholar 

  23. Kothari, A. A., & Patel, W. D. (2015). A novel approach towards context based recommendations using support vector machine methodology. Procedia Computer Science, 57, 1171–1178.

    Article  Google Scholar 

  24. Felfernig, A., & Burke, R. (2008). Constraint-based recommender systems: Technologies and research issues. In 10th International Conference on Electronic Commerce, (ICEC)’08 Innsbruck, Austria. ACM. ISBN: 978-1-60558-075-3/08/08.

    Google Scholar 

  25. Adomavicius, G., & Kwon, Y. (2015). Multi-criteria recommender systems. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook. Berlin: Springer. https://doi.org/10.1007/978-1-4899-7637-6_24.

  26. Beel, J., Langer, S., Genzmehr, M., & Nürnberger, A. (2013). Persistence in recommender systems: Giving the same recommendations to the same users multiple times. In Proceedings of the 17th International Conference on Theory and practice of Digital Libraries. LNCS. Berlin: Springer.

    Google Scholar 

  27. Reed, R., & Marks II, R. J. (1999). Neural smithing: Supervised learning in feedforward artificial neural networks. MIT Press. ISBN-13: 978-0262181907.

    Google Scholar 

  28. Waila, P., Singh, V. K., & Singh, M. K. (2016). A scientometric analysis of research in recommender systems. Journal of Scientometric Research, 5(1).

    Google Scholar 

  29. Beel, J., Langer, S., & Genzmehr, M. (2013). Sponsored vs. organic (research paper) recommendations and the impact of labeling. In T. Aalberg, C. Papatheodorou, M. Dobreva, G. Tsakonas, & C. J. Farrugia (Eds.), Research and advanced technology for digital libraries. TPDL 2013, LNCS 8092 (pp. 391–395). Berlin: Springer.

    Google Scholar 

  30. Parra, D., & Sahebi, S. (2013). Recommender systems: Sources of knowledge and evaluation metrics. In J. D. Velasquez, V. Palade, & L. Jain (Eds.), Advanced techniques in web intelligence-2. SCI 452 (pp. 149–175). Berlin: Springer.

    Google Scholar 

  31. Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16, 261–273.

    Article  Google Scholar 

  32. Marung, Ukrit, Theera-Umpon, N., & Auephanwiriyakul, S. (2016). Top-N recommender systems using genetic algorithm-based visual-clustering methods. Symmetry, 8, 54. https://doi.org/10.3390/sym8070054.

    Article  MathSciNet  Google Scholar 

  33. Kaminskas, M., & Bridge, D. (2014). Measuring surprise in recommender systems. In Workshop on Recommender Systems Evaluation: Dimensions and Design (REDD 2014), held in conjunction with RecSys 2014.

    Google Scholar 

  34. Murakami, T., Mori, K., & Orihara, R. (2008). Metrics for evaluating the serendipity of recommendation lists. K. Satoh, A. Inokuchi, K. Nagao, & T. Kawamura (Eds.), New frontiers in artificial intelligence. JSAI 2007, LNAI 4914 (pp. 40–46). Berlin: Springer.

    Google Scholar 

  35. Beel, J., Langer, S., Nürnberger, A., & Genzmehr, M. (2013). The impact of demographics (age and gender) and other user-characteristics on evaluating recommender systems. In Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries. Berlin: Springer.

    Google Scholar 

  36. Cremonesi, P., Garzotto, F., & Turrin, R. (2013). User-centric vs. system-centric evaluation of recommender systems. In P. Kotzé, G. Marsden, G. Lindgaard, J. Wesson, & M. Winckler (Eds.), INTERACT 2013, Part III. LNCS 8119 (pp. 334–351). 2013 © IFIP International Federation for Information Processing.

    Google Scholar 

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Author information

Authors and Affiliations

  1. CVR College of Engineering, Hyderabad, 501 510, Telangana, India

    R. Ponnusamy

  2. Department of Information Systems, Faculty of Informatics, University of Gondar, Gondar, Ethiopia

    Worku Abebe Degife & Tewodros Alemu

Authors
  1. R. Ponnusamy
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  2. Worku Abebe Degife
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  3. Tewodros Alemu
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Corresponding author

Correspondence to R. Ponnusamy .

Editor information

Editors and Affiliations

  1. School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India

    S. Margret Anouncia

  2. The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense M, Denmark

    Uffe Kock Wiil

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Ponnusamy, R., Degife, W.A., Alemu, T. (2018). Recommender Frameworks Outline System Design and Strategies: A Review. In: Margret Anouncia, S., Wiil, U. (eds) Knowledge Computing and its Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-8258-0_12

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  • DOI: https://doi.org/10.1007/978-981-10-8258-0_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8257-3

  • Online ISBN: 978-981-10-8258-0

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