Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Improving the Predictive Power of Business Performance Measurement Systems by Constructed Data Quality Features? Five Cases Read chapter Read first chapter

2015 | OriginalPaper | Chapter

An Unexpectedness-Augmented Utility Model for Making Serendipitous Recommendation

Authors : Qianru Zheng, Chi-Kong Chan, Horace H. S. Ip

Published in: Advances in Data Mining: Applications and Theoretical Aspects

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Many recommendation systems traditionally focus on improving accuracy, while other aspects of recommendation quality are often overlooked, such as serendipity. Intuitively, a serendipitous recommendation is one that provides a pleasant surprise, which means that a suggestion must be unexpected to the user, and yet it must be useful. Based on this principle, we propose a novel serendipity-oriented recommendation mechanism. To model unexpectedness, we combine the concepts of item rareness and dis-similarity: the less popular is an item and the further is its distance from a user’s profile, the more unexpected it is assumed to be. To model usefulness, we adopt PureSVD latent factor model, whose effectiveness in capturing user interests has been demonstrated. The effectiveness of our mechanism has been experimentally evaluated based on popular benchmark datasets and the results are encouraging: our approach produced superior results in terms of serendipity, and also leads in terms of accuracy and diversity.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Visualized Episode Mining with Feature Granularity Selection
next chapter An Approach for Predicting River Water Quality Using Data Mining Technique
Literature
1.
Adamopoulos, P., Tuzhilin, A.: On unexpectedness in recommender systems: or how to expect the unexpected. In: Proceedings of RecSys 2011, Chicago, IL, USA, 23–27 October 2011, pp. 11–18. ACM, New York (2011)
2.
Adomavicius, G., Kwon, Y.: Toward more diverse recommendations: item reranking methods for recommender systems. In: Proceedings of WITS 2009, Phoenix, AZ, USA, 14–15 December 2009. SSRN, New York (2009)
3.
Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012)CrossRef
4.
Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. ACM Commun. 40(3), 66–72 (1997)CrossRef
5.
Benjamin, W., Chandrasegaran, S., Ramanujan, D., Elmqvist, N., Vishwanathan, S., Ramani, K.: Juxtapoze: supporting serendipity and creative expression in clipart compositions. In: Proceedings of CHI 2014, Tronto, Canada, 26 April–1 May 2014, pp. 341–350. ACM, New York (2014)
6.
Cantador, I., Brusilovsky, P., Kuflik, T.: 2nd workshop on information heterogeneity and fusion in recommender systems. In: Proceedings of RecSys 2011, Chicago, IL, USA, 23–27 October 2011. ACM, New York (2011)
7.
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on Top-N recommendation tasks. In: Proceedings of RecSys 2010, Barcelona, Spain, 26–30 September 2010, pp. 39–46. ACM, New York (2010)
8.
Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of RecSys 2010, Barcelona, Spain, 26–30 September 2010, pp. 257–260. ACM, New York (2010)
9.
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRef
10.
Hurley, N., Zhang, M.: Novelty and diversity in Top-N recommendation - analysis and evaluation. ACM Trans. Internet Technol. 10(4), 14 (2011)CrossRef
11.
Bennett, J., Lanning, S.: The netflix prize. In: Proceedings of KDD Cup and Workshop, California, USA, 12 August 2007. ACM, New York (2007)
12.
Kim, H.N., Saddik, A.E., Jung, J.G.: Leveraging personal photos to inferring friendships in social network services. Expert Syst. Appl. 39(8), 6955–6966 (2012)CrossRef
13.
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of SIGKDD 2008, Las Vegas, NV, USA, 24–27 August 2008, pp. 426–434. ACM, New York (2008)
14.
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRef
15.
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRef
16.
Lucas, J.P., Luz, N., Moreno, M.N., Anacleto, R., Almeida Figueiredo, A., Martins, C.: A hybrid recommendation approach for a tourism system. Expert Syst. Appl. 40(9), 3532–3550 (2013)CrossRef
17.
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Proceedings of CHI EA 2006, Montréal, Canada, 22–27 April 2006, pp. 1097–1101. ACM, New York (2006)
18.
Murakami, T., Mori, K., Orihara, R.: Metrics for evaluating the serendipity of recommendation lists. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) JSAI 2007. LNCS (LNAI), vol. 4914, pp. 40–46. Springer, Heidelberg (2008) CrossRef
19.
Özbal, G., Karaman, H., Alpaslan, F.N.: A content-boosted collaborative filtering approach for movie recommendation based on local and global similarity and missing data prediction. Comp. J. 54(9), 1535–1546 (2011)CrossRef
20.
Padmanabhan, B., Tuzhilin, A.: A belief-driven method for discovering unexpected patterns. In: Proceedings of SIGKDD 1998, New York, NY, USA, 27–31 August 1998, pp. 94–100. AAAI Press, California (1998)
21.
Qiuxia, L., Chen, T., Zhang, W., Yang, D., Yu, Y.: Serendipitous personalized ranking for Top-N recommendation. In: Proceedings WI 2012, Macau, China, 4–7 December 2012, pp. 258–264. IEEE Computer Society, Washington (2012)
22.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of Netnews. In: Proceedings of CSCW 1994, Chapel Hill, North Carolina, USA, 22–26 October 1994, pp. 175–186. ACM, New York (1994)
23.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of WWW 2001, Hong Kong, China, 1–5 May 2001, pp. 285–295. ACM, New York (2001)
24.
Steck, H.: Training and testing of recommender systems on data missing not at random. In: Proceedings of SIGKDD 2010, Washington, DC, USA, 25–28 July 2010, pp. 713–722. ACM, New York (2010)
25.
Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of WWW 2005, Chiba, Japan, 10–14 May 2005, pp. 22–32. ACM, New York (2005)
Metadata
Title
An Unexpectedness-Augmented Utility Model for Making Serendipitous Recommendation
Authors
Qianru Zheng
Chi-Kong Chan
Horace H. S. Ip
Copyright Year
2015
Book
Advances in Data Mining: Applications and Theoretical Aspects

Print ISBN: 978-3-319-20909-8

Electronic ISBN: 978-3-319-20910-4

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-20910-4_16

Premium Partner

    Image Credits