Abstract
The exponential rise in the number of users and information has resulted in the information overhead, which restricts timely access to intended information on the Internet. In this age of Big Data, the main issues are that the data is heterogeneous, massive, less structured, and beyond the petabyte range, and hence, information retrieval is challenging. Information retrieval systems or search engines such as Google, Bing, AltaVista, and Devilfinder have partially solved this problem, but still personalization and prioritization have not been achieved absolutely. Users have to put more effort and time in searching the expected and essential information, and sometimes this may result in lower recall and precision rates. Many recommender approaches have been developed for several domains, but these are not sufficient to fulfill the need of users. So, it is important to develop a recommender system that is both precise and efficient. For developing such type of systems, many dimensions are to be given appropriate attention. This work is intended to examine and illustrate the dimensions of recommender system and also analyzes the evaluation metrics.
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Konstan, J.A., Riedl, J.: Recommender systems from algorithms to user experience. User Model User-Adapt Interact 22, 101–123. Springer Science + Business Media (2012)
Joaquin, D., Naohiro, I.: Memory-based weighted-majority prediction for recommender systems. In: 1999 SIGIR Workshop on Recommender Systems, pp. 1–5. University of California, Berkeley (1999)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22, 89–115 (2004)
Huang, C.L., Huang, W.L.: Handling sequential pattern decay: developing a two-stage collaborative recommender system. Electron. Commer. Res. Appl. 8, 117–129 (2009)
Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A., Riedl, J.: MovieLens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, Miami, Florida, USA (2003)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7, 76–80 (2003)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retrieval J. 4, 133–151 (2001)
Resnick, P., Iakovou, N., Sushak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceeding of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186 (1994)
Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: The Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 194–201 (1995)
Bobadilla, J., Ortega, F., Hernando, A., GutiéRrez, A.: Recommender systems survey. Knowl.-Based Syst. 109–132 (2013)
Shiyu, C., Yang Zhang, Z., Jiliang, T.: Streaming recommender systems. In: Proceedings of the 26th International Conference on World Wide Web ACM, pp. 381–389 (2017)
He, C., Parra, D., Verbert, K.: Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst. Appl. 9–27 (2016)
Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., et al.: Wide & deep learning for recommender systems. In: Proceeding of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10 (2016)
Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User-Adap. Inter. 22, 317–355 (2012)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 1–19 (2009)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5–53 (2004)
Iaquinta, L., Gemmis, M.D., Lops, P., Semeraro, G., Filannino, M., Molino, P.: Introducing serendipity in a content-based recommender system. In: Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems, pp. 168–173 (2008)
Ozok, A.A., Fan, Q., Norcio, A.F.: Design guidelines for effective recommender system interfaces based on a usability criteria conceptual model: results from a college student population. Behav. Inf. Technol. 29, 57–83 (2010)
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Anwar, T., Uma, V. (2019). A Review of Recommender System and Related Dimensions. In: Shukla, R.K., Agrawal, J., Sharma, S., Singh Tomer, G. (eds) Data, Engineering and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-6347-4_1
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