Short text, based on the platform of web2.0, gained rapid development in a relatively short time. Recommendation system 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.
The CFA algorithm is considered as one of the most efficient and effective algorithms being used at present REF .
Research of Collaborative Filtering Recommendation Algorithm for Short Text
Journal of Computational Chemistry
Computer Science