Proposal of a New Recommendation System that Addresses “Personalizability”
Tomohiro Yoshikawa*, Takafumi Mori**, and Takeshi Furuhashi
*Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
**Brother Industries, Ltd., 15-1 Naeshiro-cho, Mizuho-ku, Nagoya 467-8561, Japan
Users’ accessibility to a tremendous amount of information has increased because of the recent development of various media platforms and the resulting spread of the Internet. Alternatively, however, users are finding it increasingly difficult to locate meaningful data. “Recommending” information has become a popular field of study. A variety of recommendation systems are currently in use. “Accuracy” is the main performance index used to assess these systems. However, other evaluation indexes have been proposed. One particular index, “Serendipity,” provides a description of each recommended item’s newness and states whether each item contains an element of surprise. In this paper, we provide a definition of “Personalizability,” one of several factors that must be considered during an evaluation of “Serendipity.” In addition, we propose a recommendation method that employs “Personalizability,” and we apply the proposed method to benchmark data. We demonstrate that “Personalizability” can be used to tweak “Accuracy.” We also demonstrate how this method improves on conventional methods in “Personalizability.”
-  T. Kamishima, “Algorithms for Recommender Systems (1),” J. of Japanese Society for Artificial Intelligence, Vol.22, No.6, pp. 826-837, 2007 (in Japanese).
-  T. Kamishima, “Algorithms for Recommender Systems (2),” J. of Japanese Society for Artificial Intelligence, Vol.23, No.1, pp. 89-103, 2008 (in Japanese).
-  T. Kamishima, “Algorithms for Recommender Systems (3),” J. of Japanese Society for Artificial Intelligence, Vol.23, No.2, pp. 248-263, 2008 (in Japanese).
-  http://www.amazon.com
-  http://www.pandora.com
-  Y. Koren, “Tutorial on recent progress in collaborative filtering,” Proc. of the 2008 ACM Conf. on Recommender Systems, pp. 333-334, 2008.
-  J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Trans. on Information Systems, Vol.22, 2004.
-  B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” Proc. of the 10th Int. Conf. on World Wide Web, pp. 285-295, 2001.
-  W. S. Lee, “Collaborative learning for recommender systems,” Proc. of the 18th Int. Conf. on Machine Learning, pp. 314-321, 2001.
-  R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” Proc. of the 20th Int. Conf. on Very Large Data Bases, pp. 487-499, 1994.
-  L. Geng and H. J. Hamilton, “Interestingness measures for data mining: A survey,” ACM Computing Surveys, Vol.38, 2006.
-  B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Analysis of recommendation algorithms for e-commerce,” Proc. of the 2nd ACM Conf. on Electronic Commerce, pp. 158-167, 2000.
-  C. Kim and J. Kim, “A recommendation algorithm using multi-level association rules,” Proc. of the 2003 IEEE/WIC Int. Conf. on Web Intelligence, pp. 524-527, 2003.
-  C. N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, “Improving recommendation lists through topic diversification,” Proc. of the 14th Int. Conf. on World Wide Web, pp. 22-32, 2005.
-  H. Chandrashekhar and B. Bhasker, “Personalized recommender system using entropy based collaborative filtering technique,” J. of Electronic Commerce Research, Vol.12, No.3, pp. 214-237, 2011.
-  V. Schickel-Zuber and B. Faltings, “Inferring user’s preferences using ontologies,” Proc. of the 21st National Conf. on Artificial Intelligence, pp. 1413-1418, 2006.
-  K. Oku and F. Hattori, “Serendipity shikou jouhou suisen no tame no fusion-based approach no user hyouka,” The 4th Forum on Data Engineering and Information Management, A1-3, 2012 (in Japanese).
-  T. Murakami, K. Mori, and R. Orihara, “Metrics for evaluating the serendipity of recommendation lists,” JSAI 2007, Lecture Notes in Artificial Intelligence, Vol.4914, pp. 40-46, 2008.
-  M. Ge, C. Delgado-Battenfeld, and D. Jannach, “Beyond accuracy: evaluating recommender systems by coverage and serendipity,” Proc. of the fourth ACM Conf. on Recommender systems, pp. 257-260, 2010.
-  X. Yin and J. Han, “CPAR: Classification based on predictive association rules,” Proc. of the 3rd SIAM Int. Conf. on Data Mining, pp. 331-335, 2003.
-  S. Kullback, “Information theory and statistics,” Dover Publications, Mineola, N.Y., 1997.
-  B. N. Miller, I. Albert, S. K. Lam, J. A. Konstan, and J. Riedl, “Movielens unplugged: experiences with an occasionally connected recommender system,” Proc. of the 8th Int. Conf. on Intelligent User Interfaces, pp. 263-266, 2003.
-  D. Gupta, M. Digiovanni, H. Narita, and K. Goldberg, “Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm,” Proc. of the 22nd Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 291-292, 1999.
-  R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” Proc. of Int. Joint Conf. on Artificial Intelligence 1995, pp. 1137-1143, 1995.