Abstract
We address the problem of overspecialization in streaming platform recommender systems. The personalization of web pages by delivering content to users is a challenging task in data mining. But it has been proved that beside optimizing the relevance accuracy such systems should also rely on other factors like diversity or novelty. In this paper we focus on modeling users’ boundary area of interest by selecting the most diverse items they liked in the past. We apply diversification while building the top-N list of recommendations. We select the items we want to recommend from an area where we consider a user will find item different from what she or he likes in the past. We evaluate our approach in offline analysis on two datasets, showing that our approach brings diversity and is competitive against implicit state-of-the-art method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbassi, Z., Amer-Yahia, S., Lakshmanan, L.V.S., Vassilvitskii, S., Yu, C.: Getting recommender systems to think outside the box. In: Bergman, L.D., Tuzhilin, A., Burke, R.D., Felfernig, A., Schmidt-Thieme, L. (eds.) Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, New York, NY, USA, 23–25 October 2009, pp. 285–288. ACM (2009)
Adamopoulos, P., Tuzhilin, A.: On unexpectedness in recommender systems: or how to better expect the unexpected. ACM Trans. Intell. Syst. Technol. (TIST) 5(4), 54:1–54:32 (2014)
Anderson, C.: The Longer Long Tail: How Endless Choice is Creating Unlimited Demand. Random House Business, New York (2009)
Ashkan, A., Kveton, B., Berkovsky, S., Wen, Z.: Optimal greedy diversity for recommendation. In: Yang, Q., Wooldridge, M. (eds.) Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, 25–31 July 2015, pp. 1742–1748. AAAI Press (2015)
Borodin, A., Lee, H.C., Ye, Y.: Max-sum diversification, monotone submodular functions and dynamic updates. CoRR abs/1203.6397 (2012)
Cantador, I., Brusilovsky, P., Kuflik, T.: Second workshop on information heterogeneity and fusion in recommender systems (HetRec 2011). In: Proceedings of the 5th ACM conference on Recommender systems, RecSys 2011, NY, USA. ACM, New York (2011)
Carbonell, J.G., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Croft, W.B., Moffat, A., van Rijsbergen, C.J., Wilkinson, R., Zobel, J. (eds.) Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, Melbourne, Australia, 24–28 August 1998, pp. 335–336. ACM (1998)
Castells, P., Hurley, N.J., Vargas, S.: Novelty and Diversity in Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 881–918. Springer, Boston (2015). doi:10.1007/978-1-4899-7637-6_26
Celma, Ò.: Music recommendation and discovery in the long tail. Ph.D. thesis, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain (2008)
Celma, Ò.: Music Recommendation and Discovery – The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer, Heidelberg (2010)
Ekstrand, M.D., Harper, F.M., Willemsen, M.C., Konstan, J.A.: User perception of differences in recommender algorithms. In: Eighth ACM Conference on Recommender Systems, RecSys 2014, Foster City, Silicon Valley, CA, USA, 6–10 October 2014, pp. 161–168 (2014)
Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Amatriain, X., Torrens, M., Resnick, P., Zanker, M. (eds.) Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, Barcelona, Spain, 26–30 September 2010, pp. 257–260. ACM (2010)
Hahsler, M.: recommenderlab: Lab for Developing and Testing Recommender Algorithms (2017). Rpackageversion0.2-2. http://lyle.smu.edu/IDA/recommenderlab/
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TiiS) 5(4), 19:1–19:19 (2016)
Jannach, D., Adomavicius, G.: Recommendations with a purpose. In: Sen, S., Geyer, W., Freyne, J., Castells, P. (eds.) Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 15–19 September 2016, pp. 7–10. ACM (2016)
Kohonen, T.: Self-organizing Maps. Springer Series in Information Sciences, 3rd edn. Springer, Heidelberg (2001)
Marr, D., Hildreth, E.C.: Theory of edge detection. Proc. R. Soc. Lond. B, Biol. Sci. 207(1167), 187–217 (1980)
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Olson, G.M., Jeffries, R. (eds.) Extended Abstracts Proceedings of the 2006 Conference on Human Factors in Computing Systems, CHI 2006, Montréal, Québec, Canada, 22–27 April 2006, pp. 1097–1101. ACM (2006)
Mulligan, M.: The death of the long tail: the superstar music economy, mIDiA Consulting internal report. https://musicindustryblog.wordpress.com/2014/03/04/the-death-of-the-long-tail/
Niemann, K., Wolpers, M.: A new collaborative filtering approach for increasing the aggregate diversity of recommender systems. In: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, 11–14 August 2013, pp. 955–963 (2013)
Noia, T.D., Rosati, J., Tomeo, P., Sciascio, E.D.: Adaptive multi-attribute diversity for recommender systems. Inf. Sci. 382–383, 234–253 (2017)
Parambath, S.P., Usunier, N., Grandvalet, Y.: A coverage-based approach to recommendation diversity on similarity graph. In: Sen, S., Geyer, W., Freyne, J., Castells, P. (eds.) Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 11–14 September 2016, pp. 15–22. ACM (2016)
Pariser, E.: The Filter Bubble: What The Internet Is Hiding From You. Penguin Press, New York (2011)
Ribeiro, M.T., Lacerda, A., Veloso, A., Ziviani, N.: Pareto-efficient hybridization for multi-objective recommender systems. In: Sixth ACM Conference on Recommender Systems, RecSys 2012, Dublin, Ireland, 9–13 September 2012, pp. 19–26 (2012)
Ricker, N.: Wavelet functions and their polynomials. Geophysics 9(3), 314–323 (1944)
Schwartz, B.: The Paradox of Choice - Why More Is Less. Harper Perennial, New York (2004)
Seyerlehner, K., Flexer, A., Widmer, G.: On the limitations of browsing top-N recommender systems. In: Bergman, L.D., Tuzhilin, A., Burke, R.D., Felfernig, A., Schmidt-Thieme, L. (eds.) Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, New York, NY, USA, 23–25 October 2009, pp. 321–324. ACM (2009)
Smyth, B., McClave, P.: Similarity vs. diversity. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS, vol. 2080, pp. 347–361. Springer, Heidelberg (2001). doi:10.1007/3-540-44593-5_25
Steck, H.: Item popularity and recommendation accuracy. In: Mobasher, B., Burke, R.D., Jannach, D., Adomavicius, G. (eds.) Proceedings of the 2011 ACM Conference on Recommender Systems, RecSys 2011, Chicago, IL, USA, 23–27 October 2011, pp. 125–132. ACM (2011)
Vargas, S.: Novelty and diversity evaluation and enhancement in recommender systems. Ph.D. thesis, Universidad Autonoma de Madrid, Spain, February 2015
Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the 2011 ACM Conference on Recommender Systems, RecSys 2011, Chicago, IL, USA, 23–27 October 2011, pp. 109–116 (2011)
Vargas, S., Castells, P.: Exploiting the diversity of user preferences for recommendation. In: Ferreira, J., Magalhães, J., Calado, P. (eds.) Open research Areas in Information Retrieval, OAIR 2013, Lisbon, Portugal, 15–17 May 2013, pp. 129–136. ACM (2013)
Wasilewski, J., Hurley, N.: Intent-aware diversification using a constrained PLSA. In: Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 15–19 September 2016, pp. 39–42 (2016)
Yang, M.-H., Gu, Z.-M.: Personalized recommendation based on partial similarity of interests. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 509–516. Springer, Heidelberg (2006). doi:10.1007/11811305_56
Zhang, J., Zhu, X., Li, X., Zhang, S.: Mining item popularity for recommender systems. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds.) ADMA 2013. LNCS (LNAI), vol. 8347, pp. 372–383. Springer, Heidelberg (2013). doi:10.1007/978-3-642-53917-6_33
Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: Pu, P., Bridge, D.G., Mobasher, B., Ricci, F. (eds.) Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, Lausanne, Switzerland, 23–25 October 2008, pp. 123–130. ACM (2008)
Zhang, M., Hurley, N.: Novel item recommendation by user profile partitioning. In: 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009, Milan, Italy, 15–18 September 2009, Main Conference Proceedings. pp. 508–515. IEEE Computer Society (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Lhérisson, PR., Muhlenbach, F., Maret, P. (2017). Fair Recommendations Through Diversity Promotion. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-69179-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69178-7
Online ISBN: 978-3-319-69179-4
eBook Packages: Computer ScienceComputer Science (R0)