the results, for example, the subject's degree of
knowledge of a particular topic, the certainty in what
he or she is looking for and the objective fitness
criteria of objects for the searcher's purpose.
ACKNOWLEDGEMENTS
Parts of the work presented in this paper have been
supported by the German Federal Ministry of
Research (BMBF) by a grant under the KMU
Innovativ program as part of the Intelligent Match
project (FKZ 01IS10022B).
REFERENCES
Adomavicius, G., Kwon, Y., 2011a, Improving aggregate
recommendation diversity using ranking-based
techniques. IEEE Transactions on Knowledge and
Data Engineering, 99, 1-15.
Adomavicius, G., Kwon, Y., 2011b. Maximizing
aggregate recommendation diversity: a graph-theoretic
approach, In Proceedings of Workshop on Novelty and
Diversity in Recommender Systems, Chicago, Illinois,
USA, 3-10.
Adamopoulos, P., and Tuzhilin, A., 2011. On
unexpectedness in recommender systems: or how to
expect the unexpected, In Proceedings of Workshop on
Novelty and Diversity in Recommender Systems,
Chicago, Illinois, USA.
Castells, P., Vargas, S., Wang, J., 2011. Novelty and
diversity metrics for recommender systems: choice,
discovery and relevance. In Proceedings of
International Workshop on Diversity in Document
Retrieval, Dublin, Ireland, 29-37.
Clarke, C. L. A., Craswell, N., Soboroff, I. and Ashkan,
A., 2011. A comparative analysis of cascade measures
for novelty and diversity, In Proceedings of the fourth
ACM international conference on web search and data
mining, Hong Kong, China, 75-84.
Dias, M. B., Locher, D., Li, M., El-Deredy,W. and Lisboa,
P. J., 2008. The value of personalised recommender
systems to e-business: a case study. In Proceedings of
the 2008 ACM Conference on Recommender Systems,
Lausanne, Switzerland, 291–294.
Fleder, D., Hosanagar, K., 2007, Recommender systems
and their impact on sales diversity. In Proceedings of
the 8th ACM Conference on Electronic Commerce,
San Diego, CA, USA, 192-199.
Ge, M., Delgado-Battenfeld, C., and Jannach, D., 2010.
Beyond accuracy: evaluating recommender systems by
coverage and serendipity. In Proceedings of the fourth
ACM Conference on Recommender Systems, New
York, 257-260.
Herlocker, L., Konstan, J., Terveen, L., Riedl, J., 2004.
Evaluating collaborative filtering recommender
systems, ACM Transactions on Information Systems
22,1: 5-53
Jannach, D., Hegelich K., 2009. A case study on the
effectiveness of recommendations in the mobile
Internet, ACM Conference on Recommender Systems,
New York, 205-208.
Jannach, D., Zanker, M., Felfernig, A., Friedrich G., 2010.
Recommender systems: an Introduction, Cambridge
University Press.
Lathia, N., Hailes, S., Capra, L., Amatriain, X., 2010.
Temporal diversity in recommender systems. In
Proceedings of the 33rd International ACM SIGIR
Conference on Research and Development in
Information Retrieval, Geneva, Switzerland, 210-217.
McNee, S, Riedl, J., Konstan, J., 2006. Being accurate is
not enough: how accuracy metrics have hurt
recommender systems, In Proceedings of the ACM
SIGCHI Conference on Human Factors in Computing
Systems. Montréal, Canada, 1097-1101.
Smyth, B. and McClave, P., 2001. Similarity vs. Diversity.
In Proceedings of 4th International Conference on
Case-Based Reasoning, Vancouver, Canada, 348-361.
Sakai, T., 2011. Challenges in diversity evaluation, In
Proceedings of International Workshop on Diversity
in Document Retrieval. Dublin, Ireland, 1-7.
Zanker, M., Bricman, M., Gordea, S., Jannach, D.,
Jessenitschnig, M., 2006. Persuasive online selling in
quality & taste domains, Proceedings EC-Web'06,
Krakow, Poland, Springer LNCS 4082.
Zhou, T., Kuscsika, Z., Liua, J., Medoa, M., Wakelinga, J.,
Zhang. Y., 2010. Solving the apparent diversity-
accuracy dilemma of recommender systems. National
Academy of Sciences of the USA. 107, 10, 4511-4515.
Zhang, M., Hurley, N., 2008. Avoiding monotony:
improving the diversity of recommendation lists. In
Proceedings of the 2nd ACM conference on
recommender Systems, Lausanne, Switzerland, 123-
130.
Ziegler, C., McNee, S., Konstan, J., Lausen, G., 2005.
Improving Recommendation Lists through Topic
Diversification. In Proceedings of the 14th World
Wide Web Conference. Chiba, Japan, 22-32.
ICEIS2012-14thInternationalConferenceonEnterpriseInformationSystems
208