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International Workshop on Complex Networks and their Applications

COMPLEX NETWORKS 2016 2016: Complex Networks & Their Applications V pp 247–259Cite as

A Method for Evaluating the Navigability of Recommendation Algorithms

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 693))

Abstract

Recommendations are increasingly used to support and enable discovery, browsing and exploration of large item collections, especially when no clear classification of items exists. Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any evaluation measures proposed so far. In this paper, we propose a method to expand the repertoire of existing recommendation evaluation techniques with a method to evaluate the navigability of recommendation algorithms. The proposed method combines approaches from network science and information retrieval and evaluates navigability by simulating three different models of information seeking scenarios and measuring the success rates. We show the feasibility of our method by applying it to four non-personalized recommendation algorithms on three datasets and also illustrate its applicability to personalized algorithms. Our work expands the arsenal of evaluation techniques for recommendation algorithms, extends from a one-click-based evaluation towards multi-click analysis and presents a general, comprehensive method to evaluating navigability of arbitrary recommendation algorithms.

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References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB’94 (1994)

    Google Scholar 

  2. Bates, M.J.: The design of browsing and berrypicking techniques for the online search interface. Online Information Review 13(5), 407–424 (1989)

    Google Scholar 

  3. Bell, R.M., Koren, Y.: Improved neighborhood-based collaborative filtering. In: KDD Cup and Workshop at the KDD’07 (2007)

    Google Scholar 

  4. Boim, R., Milo, T., Novgorodov, S.: Diversification and refinement in collaborative filtering recommender. In: CIKM’11 (2011)

    Google Scholar 

  5. Bollen, D., Knijnenburg, B.P., Willemsen, M.C., Graus, M.: Understanding choice overload in recommender systems. In: RecSys’04. ACM (2010)

    Google Scholar 

  6. Cano, P., Celma, O., Koppenberger, M., Buld´u, J.M.: Topology of music recommendation networks. Chaos 16(1) (2006)

    Google Scholar 

  7. Castells, P., Hurley, N.J., Vargas, S.: Novelty and diversity in recommender systems. In: Recommender Systems Handbook, pp. 881–918. Springer (2015)

    Google Scholar 

  8. Celma, Ò., Herrera, P.: A new approach to evaluating novel recommendations. In: RecSys’08 (2008)

    Google Scholar 

  9. Chi, E.H., Pirolli, P., Chen, K., Pitkow, J.: Using information scent to model user information needs and actions on the web. In: CHI’01 (2001)

    Google Scholar 

  10. Davidson, J., Liebald, B., Liu, J., Nandy, P., Vleet, T.V., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., Sampath, D.: The youtube video recommendation system. In: RecSys’10 (2010)

    Google Scholar 

  11. Gunawardana, A., Shani, G.: Evaluating recommender systems. In: Recommender Systems Handbook, pp. 265–308. Springer (2015)

    Google Scholar 

  12. Helic, D., Strohmaier, M., Granitzer, M., Scherer, R.: Models of human navigation in information networks based on decentralized search. In: HT’13 (2013)

    Google Scholar 

  13. Helic, D., Strohmaier, M., Trattner, C., Muhr, M., Lerman, K.: Pragmatic evaluation of folksonomies. In: WWW’11 (2011)

    Google Scholar 

  14. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)

    Google Scholar 

  15. Kleinberg, J.M.: Navigation in a small world. Nature 406(6798), 845 (2000)

    Google Scholar 

  16. Kleinberg, J.M.: The small-world phenomenon: An algorithmic perspective. In: TOC’00 (2000)

    Google Scholar 

  17. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Google Scholar 

  18. Lamprecht, D., Dimitrov, D., Helic, D., Strohmaier, M.: Evaluating and improving navigability of wikipedia: A comparative study of eight language editions. In: OpenSym’16 (2016)

    Google Scholar 

  19. Lamprecht, D., Geigl, F., Karas, T., Walk, S., Helic, D., Strohmaier, M.: Improving recommender system navigability through diversification: A case study of IMDb. In: IKNOW’15 (2015)

    Google Scholar 

  20. Lamprecht, D., Strohmaier, M., Helic, D., Nyulas, C., Tudorache, T., Noy, N.F., Musen, M.A.: Using ontologies to model human navigation behavior in information networks: A study based on wikipedia. Semantic Web 6(4), 403–422 (2015)

    Google Scholar 

  21. Lerman, K., Jones, L.: Social Browsing on Flickr. In: ICWSM’07 (2007)

    Google Scholar 

  22. Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)

    Google Scholar 

  23. Milgram, S.: The small world problem. Psychology Today 1(2), 60–67 (1967)

    Google Scholar 

  24. Nguyen, T.T., Hui, P.M., Harper, M.F., Terveen, L., Konstan, J.A.: Exploring the filter bubble: the effect of using recommender systems on content diversity. In: WWW’14 (2014)

    Google Scholar 

  25. Nielsen, J.: The 90-9-1 rule for participation inequality in social media and online communities (2006). www.nngroup.com/articles/participation-inequality

  26. Nonnecke, B., Preece, J.: Lurker demographics: Counting the silent. In: CHI’00 (2000)

    Google Scholar 

  27. Pirolli, P.: Information Foraging Theory: Adaptive Interaction with Information. Oxford University Press (2007)

    Google Scholar 

  28. Seyerlehner, K., Knees, P., Schnitzer, D., Widmer, G.: Browsing music recommendation networks. In: ISMIR’09 (2009)

    Google Scholar 

  29. Teevan, J., Alvarado, C., Ackerman, M.S., Karger, D.R.: The perfect search engine is not enough: A study of orienteering behavior in directed search. In: CHI’04 (2004)

    Google Scholar 

  30. Toms, E.G.: Serendipitous information retrieval. In: DELOS Workshop: Information Seeking, Searching and Querying in Digital Libraries (2000)

    Google Scholar 

  31. Trattner, C., Singer, P., Helic, D., Strohmaier, M.: Exploring the differences and similarities between hierarchical decentralized search and human navigation in information networks. In: IKNOW’12 (2012) A Method for Evaluating the Navigability of Recommendation Algorithms 259

    Google Scholar 

  32. Watts, D.J., Dodds, P.S., Newman, M.: Identity and search in social networks. Science 296, 1302–1305 (2002)

    Google Scholar 

  33. Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW’05 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. KTI, Graz University of Technology, Graz, Austria

    Daniel Lamprecht & Denis Helic

  2. GESIS, University of Koblenz-Landau, Koblenz, Germany

    Markus Strohmaier

Authors
  1. Daniel Lamprecht
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  2. Markus Strohmaier
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  3. Denis Helic
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Corresponding authors

Correspondence to Daniel Lamprecht , Markus Strohmaier or Denis Helic .

Editor information

Editors and Affiliations

  1. University of Burgundy , Dijon, France

    Hocine Cherifi

  2. Computer Science Department, University of Milan Computer Science Department, Milan, Italy

    Sabrina Gaito

  3. IMT Lucca , Lucca, Italy

    Walter Quattrociocchi

  4. Blanchardstown Business and Tech Park, Bell Labs-Nokia Blanchardstown Business and Tech Park, Blanchardstown, Ireland

    Alessandra Sala

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Lamprecht, D., Strohmaier, M., Helic, D. (2017). A Method for Evaluating the Navigability of Recommendation Algorithms. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-50901-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50900-6

  • Online ISBN: 978-3-319-50901-3

  • eBook Packages: EngineeringEngineering (R0)

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