The long tail of recommender systems and how to leverage it

ABSTRACT

The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.

REFERENCES

• 1: Andrew I. Schein , Alexandrin Popescul , Lyle H. Ungar , David M. Pennock, Methods and metrics for cold-start recommendations, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564421]
• 2: Anderson, C. 2006. The Long Tail. Hyperion press.
• 3: Fleder, D. M., and Hosanagar, K. 2008. Blockbuster Cultures Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. NET Institute Working Paper No. #07-10.
• 4: Hervas-Drane, A. 2007. Word of Mouth and Recommender Systems: A Theory of the Long Tail. NET Institute Working Paper No.07-41, November 2007.
• 5: http://movielens.umn.edu.
• 6: http://www.bookcrossing.com.
• 7: Ian H. Witten , Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems), Morgan Kaufmann Publishers Inc., San Francisco, CA, 2005
• 8: Truong, K. Q., Ishikawa, F., Honiden, S. 2007. Improving Accuracy of Recommender System by Item Clustering, IEICE TRANSACTIONS on Information and Systems, E90-D-I(9).
• 9: Ungar, L. H. and Foster, D. P. 1998. Clustering Methods for Collaborative Filtering. Proceedings of the Workshop on Recommendation Systems. AAAI Press.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

General Terms:
Algorithms, Experimentation, Performance

Keywords:
clustering, data mining, long tail, recommendation