Abstract
As the Internet has been commonly used in our everyday lives, we have been able to obtain large amount of information from it, whereas we have simultaneously had a problem that it is difficult to find proper information for us from the large amount of information on the Web. Although many information recommendation methods have been proposed in order to solve this problem, most recommendation methods are based on a large amount of user’s personal data such as operation log, schedule, etc – which means that we have to manage a large amount of personal data in the system in order to provide proper information to users, and it would be expensive to construct such a system.
With this background, in this study, against aiming to construct a sophisticated information recommendation system based on large personal data, we propose a handy and not expensive information recommendation method, working beside a normal search engine, which does not depend on user profile data, but on topical news information.
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References
Mukai, M. and Aono, M., “A Prototype of Content-based Recommendation System based on RSS,” IPSJ-NL05169005, No. 94, 2005-NL-169, pp. 27-32, Sep. 2005.
Terano, T., “Information Recommendation System on the Web,” IPSJ Magazine, Vol. 44-7, pp. 696-701, July 2003.
Hijikata, Y., “User Profiling Technique for Information Recommendation and Information Filtering,” Journal of Japanese Society for Artificial Intelligence, Vol. 19-3, pp. 265-372, 2004.
Billsus, D. and Pazzani, M., “Adaptive news access,” The Adaptive Web: Methods and Strategies of Web Personalization (Brusilovsky, P., Kobsa, A. and Nejdl, W. eds.) Springer, Berlin, 2007.
Ahn, J.W., Brusilovsky, P., Grady, J., He, D., Syn, S.Y., “Personalization: Open user profiles for adaptive news systems: help or harm?” Proc. of the 16 th int’l conf. on World Wide Web WWW ’07 , 2007.
Shardanand, U. and Maes, P., “Social information filtering: algorithms for automating “word of mouth”,” in Proc. of the ACM CHI Conf., 1995.
Joachims, T., Freitag, D., Mitchell, T., “WebWatcher: A tour guide for the World Wide Web,” in Proc. of the Int’l Joint Conf. in AI (IJCAI97), August 1997.
Marko, B. and Yoav, S., “Fab: Content-Based, Collaborative Recommendation,” CACM, Vol. 40-3, pp.66-72, 1997.
Goldberg, D., Nichols, D., Oki, B.M. and Douglas, T., “Using Social and Content-based Information in Recommendation,” Proc. of Recommender System Workshop, pp. 11-15, 1998.
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D. and Sartin, M., “Combining Content-Based and Collaborative Filters in an On-line Newspaper,” Proc. of Recommender System Workshop at ACM SIGIR, 1999.
Paul, R., Neophytos, I., Mitesh, S., Peter, B. and Riedel, J., “GroupLens: an open architecture for collaborative filtering of netnews,” Proc. of the CSCW, ACM, pp. 175-186, 1994.
Shardanand, U. and Maes, P., “Social Information Filtering: Algorithm for Automating “Word of Mouth”,” Proc. of CHI ’95, pp. 210-217, 1995.
Ebbinghaus, H., Memory: A Contribution to Experimental Psychology, Dover Publications, New York, 1964,
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Kobayashi, I., Saito, M. A Study on an Information Recommendation System that Provides Topical Information Related to User’s Inquiry for Information Retrieval. New Gener. Comput. 26, 39–48 (2007). https://doi.org/10.1007/s00354-007-0033-5
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DOI: https://doi.org/10.1007/s00354-007-0033-5