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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52497#.VJog-cCAM4
Author(s)
Short text, based on the platform of web2.0, gained
rapid development in a relatively short time. Recommendation systems
analyzing user’s interest by short texts becomes more and more
important. Collaborative filtering is one of the most promising
recommendation technologies. However, the existing collaborative
filtering methods don’t consider the drifting of user’s interest. This
often leads to a big difference between the result of recommendation and
user’s real demands. In this paper, according to the traditional
collaborative filtering algorithm, a new personalized recommendation
algorithm is proposed. It traced user’s interest by using Ebbinghaus
Forgetting Curve. Some experiments have been done. The results
demonstrated that the new algorithm could indeed make a contribution to
getting rid of user’s overdue interests and discovering their real-time
interests for more accurate recommendation.
Cite this paper
Chao, C. , Qu, S. and Du, T. (2014) Research of
Collaborative Filtering Recommendation Algorithm for Short Text. Journal of Computer and Communications, 2, 59-66. doi: 10.4236/jcc.2014.214006.
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