Effects of the Placement of Diverse Items in Recommendation Lists

Mouzhi Ge, Dietmar Jannach, Fatih Gedikli, Martin Hepp

2012

Abstract

Over the last fifteen years, a large amount of research in recommender systems was devoted to the development of algorithms that focus on improving the accuracy of recommendations. More recently, it has been proposed that accuracy is not the only factor that contributes to the quality of recommender systems. Among others, the diversity of recommendation lists has been considered as one of the additionally relevant factors. Therefore a number of algorithms were proposed to generate recommendations lists containing a diverse set of items. However, limited research has been done regarding how to position those diverse items in the list. In this paper we therefore investigate how to organize the diverse items to achieve a higher perceived quality. The results of an experimental study show that the perceived diversity of a recommendation list depends on the placement of the diverse items. Placing the diverse items dispersedly or together at the bottom of the list can increase the perceived diversity. In addition, we found that in the movie domain including diverse items in the recommendation list does not hurt user satisfaction, which means that recommender system providers have some flexibility to add some extra items to the lists, for example to increase the serendipity of the recommendations.

References

  1. Adomavicius, G., Kwon, Y., 2011a, Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering, 99, 1-15.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Herlocker, L., Konstan, J., Terveen, L., Riedl, J., 2004. Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems 22,1: 5-53
  10. 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.
  11. Jannach, D., Zanker, M., Felfernig, A., Friedrich G., 2010. Recommender systems: an Introduction, Cambridge University Press.
  12. 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.
  13. 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.
  14. Smyth, B. and McClave, P., 2001. Similarity vs. Diversity. In Proceedings of 4th International Conference on Case-Based Reasoning, Vancouver, Canada, 348-361.
  15. Sakai, T., 2011. Challenges in diversity evaluation, In Proceedings of International Workshop on Diversity in Document Retrieval. Dublin, Ireland, 1-7.
  16. 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.
  17. Zhou, T., Kuscsika, Z., Liua, J., Medoa, M., Wakelinga, J., Zhang. Y., 2010. Solving the apparent diversityaccuracy dilemma of recommender systems. National Academy of Sciences of the USA. 107, 10, 4511-4515.
  18. 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.
  19. 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.
Download


Paper Citation


in Harvard Style

Ge M., Jannach D., Gedikli F. and Hepp M. (2012). Effects of the Placement of Diverse Items in Recommendation Lists . In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8565-11-2, pages 201-208. DOI: 10.5220/0003974802010208


in Bibtex Style

@conference{iceis12,
author={Mouzhi Ge and Dietmar Jannach and Fatih Gedikli and Martin Hepp},
title={Effects of the Placement of Diverse Items in Recommendation Lists},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2012},
pages={201-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003974802010208},
isbn={978-989-8565-11-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Effects of the Placement of Diverse Items in Recommendation Lists
SN - 978-989-8565-11-2
AU - Ge M.
AU - Jannach D.
AU - Gedikli F.
AU - Hepp M.
PY - 2012
SP - 201
EP - 208
DO - 10.5220/0003974802010208