Абстрактный

Precise Recommendation System for the Long Tail Problem Using Adaptive Clustering Technique

S.Jeyshirii , Dr.T.K.Thivakaran

The Total Sale of large number of non-hit items is called “the long tail”. Long Tail Problem is one major issue in providing effective recommendation system; since long tail problem have only a few ratings. To solve this issue the proposed system identifies the clustering of items according to their popularity, It is identified that tail items are clustered based on the ratings of clustered groups while the head items are based on the ratings of individual item or groups. This method is applied to movie lens data sets and the results are compared with those of the non grouping and fully grouped methods in terms of recommendation accuracy and scalability. The results show that by implementing proposed adaptive clustering technique it reduces the recommendation error rates for the tail items while maintaining reasonable computational performance