Improving recommendation lists through topic diversification


  • Accuracy is not enough
  • Introduce intra-list similarity metric to measure diversity which improves user satisfaction
  • Propose topic diversification method to increase diversity

The Intra-List Similarity Metric and the process of Topic Diversification for recommendation lists. Returned lists can be altered to either increase or decrease the diversity of items on that list.


Results showed that these altered lists performed worse on accuracy measures than unchanged lists, but users preferred the altered lists.

Results for single-vote averages (a), covered range of interests (b), and overall satisfaction (c)


C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, “Improving recommendation lists through topic diversification,” in Proceedings of the 14th international conference on World Wide Web  - WWW ’05, Chiba, Japan, 2005, p. 22. doi: 10.1145/1060745.1060754.