@inproceedings{ziegler2005improving,
  abstract = {In this work we present topic diversification, a novel method designed to balance and diversify personalized recommenda- tion lists in order to reflect the user�s complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with rec- ommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm. Our work builds upon prior research on recommender sys- tems, looking at properties of recommendation lists as en- tities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diver- sity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evalu- ate our method using book recommendation data, including online analysis on 361, 349 ratings and an online study in- volving more than 2, 100 subjects.},
  address = {Chiba, Japan},
  author = {Ziegler, Cai-Nicolas and McNee, Sean and Konstan, Joseph and Lausen, Georg},
  booktitle = {Proceedings of the 14th International World Wide Web Conference},
  file = {ziegler2005improving.pdf:ziegler2005improving.pdf:PDF},
  interhash = {0a7f89e65c4a0a5e45aa69a54a5600e6},
  intrahash = {1c70855a788c17e3a94a7ecc00177f6c},
  lastdatemodified = {2006-09-30},
  lastname = {Ziegler},
  month = May,
  own = {notown},
  pdf = {null},
  publisher = {ACM Press},
  read = {notread},
  title = {Improving Recommendation Lists Through Topic Diversification},
  year = 2005
}

@article{ziegler2006computing,
  author = {Ziegler, Cai Nicolas and Simon, Kai and Lausen, Georg},
  interhash = {74d9b7d218e52533d19ccacfd6d8e948},
  intrahash = {bd5647a471cc104c17726488c43fa7f3},
  journal = {Proceedings of the WWW2006},
  lastdatemodified = {2006-11-29},
  lastname = {Ziegler},
  note = {submitted},
  own = {notown},
  read = {notread},
  title = {Computing Semantic Proximity Between Concepts Using Taxonomic Knowledge},
  year = 2006
}