Abstract
Collaborative Filtering is a useful algorithm to offer personalized recommendations for users. However, there are several technical challenges in collaborative filtering, including the first-rater problem, where an item not yet evaluated cannot be recommended until it has been rated. In the paper, the presenting method deals with the first-rater problem that is similar to the process starvation is operating systems. The method reduces the score gap between items and makes it possible for a new item or an item with no user preference to be recommended automatically. Thus, the system can recommend items in the same group without bias. Finally, we present an analysis of an example of the algorithm.
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References
Melville, P., Vikas, S.: Recommender Systems. Encyclopedia of Machine Learning, pp. 829–838. Springer, US (2011)
Son, J., Kim, S.B., Kim, H., Cho, S.: Review and analysis of recommender systems. J. Korean Inst. Ind. Eng. 41(2), 185–208 (2015)
Su, X., Taghi, M.K.: A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, pp. 1–19
Kumar, A., Bhatia, M.: Community expert based recommendation. J. Com. App. 37, 7–13 (2012)
Lenhart, P., Herzog, D.: Combining Content-based and Collaborative Filtering for Personalized Sports News Recommendations. In: CBRecSys, pp. 3–10 (2016)
Lee, S.G., Lee, B.S., Bak, B.Y., Hwang, H.K.: A study of intelligent recommendation system based on naive bayes text classification and collaborative filtering. J. Inf. Manage. 41(4), 227–249 (2010)
Shinde, K.S., Kulkarni, U.: Hybrid personalized recommender system using centering-bunching based clustering algorithm. Expert Syst. Appl. 39, 1381–1387 (2012)
Choi, Y.S., Moon, B.R.: A prediction system of user preferences for newly released items based on words. J. Korean Inst. Intell. Syst. 16(2), 156–163 (2006)
Acknowledgement
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0115-16-1009, Development of smart learning interaction contents for acquiring foreign languages through experiential awareness).
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Hong, Yj., Lee, S., Park, Yh. (2018). A Method to Maintain Item Recommendation Equality Among Equivalent Items in Recommender Systems. In: Lee, W., Choi, W., Jung, S., Song, M. (eds) Proceedings of the 7th International Conference on Emerging Databases. Lecture Notes in Electrical Engineering, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-10-6520-0_22
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DOI: https://doi.org/10.1007/978-981-10-6520-0_22
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