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Journal of Interactive Media

Editor-in-Chief: Ziegler, Jürgen

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Volume 14, Issue 1 (Apr 2015)

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Merging Interactive Information Filtering and Recommender Algorithms – Model and Concept Demonstrator

Benedikt Loepp
  • Corresponding author
  • University of Duisburg-Essen, Germany
  • Email:
/ Katja Herrmanny
  • University of Duisburg-Essen, Germany
  • Email:
/ Prof. Dr.-Ing. Jürgen Ziegler
  • University of Duisburg-Essen, Germany
  • Email:
Published Online: 2015-04-01 | DOI: https://doi.org/10.1515/icom-2015-0006

Abstract

To increase controllability and transparency in recommender systems, recent research has been putting more focus on integrating interactive techniques with recommender algorithms. In this paper, we propose a model of interactive recommending that structures the different interactions users can have with recommender systems. Furthermore, as a novel approach to interactive recommending, we describe a technique that combines faceted information filtering with different algorithmic recommender techniques. We refer to this approach as blended recommending. We also present an interactive movie recommender based on this approach and report on its user-centered design process, in particular an evaluation study in which we compared our system with a standard faceted filtering system. The results indicate a higher level of perceived user control, more detailed preference settings, and better suitability when the search goal is vague.

Keywords: Models; Recommender Systems; Interactive Recommending; Information Filtering; User Interfaces

References

  • 1.

    Baeza-Yates, R., and Ribeiro-Neto, B. Modern Information Retrieval. ACM, 1999.

  • 2.

    Bostandjiev, S., O’Donovan, J., and Höllerer, T. Taste-Weights: A visual interactive hybrid recommender system. In Proc. RecSys ‘12, ACM (2012), 35–42.

  • 3.

    Brooke, J. SUS – A quick and dirty usability scale. In Usability Evaluation in Industry. Taylor & Francis, 1996, 189–194.

  • 4.

    Burke, R. Hybrid web recommender systems. In The Adaptive Web. Methods and Strategies of Web Personalization, P. Brusilovsky, A. Kobsa and W. Nejdl, Eds., Springer, 2007, 377–408.

  • 5.

    Celik, I., Abel, F., and Siehndel, P. Towards a framework for adaptive faceted search on twitter. In Proc. DAH ’11 (2011).

  • 6.

    Chen, L., and Pu, P. Critiquing-based recommenders: Survey and emerging trends. User Mod. and User-Adapted Interaction 22, 1–2 (2012), 125–150. [Web of Science]

  • 7.

    Chi, E. H. Transient user profiling. In Proc. Workshop on User Profiling (2004), 521–523.

  • 8.

    Dooms, S., de Pessemier, T., and Martens, L. Improving IMDb movie recommendations with interactive settings and filters. In Proc. RecSys ‘14, ACM (2014).

  • 9.

    Gantner, Z., Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L. MyMediaLite: A free recommender system library. In Proc. RecSys ‘11, ACM (2011), 305–308.

  • 10.

    Girgensohn, A., Shipman, F., Chen, F., and Wilcox, L. DocuBrowse: Faceted searching, browsing, and recommendations in an enterprise context. In Proc. IUI ‘10, ACM (2010), 189–198.

  • 11.

    Hearst, M. A. Search User Interfaces. Cambridge University Press, 2009.

  • 12.

    Herrmanny, K., Schering, S., Berger, R., Loepp, B., Günter, T., Hussein, T., and Ziegler, J. MyMovieMixer: Ein hybrider Recommender mit visuellem Bedienkonzept. In Proc. Mensch & Computer ‘14, De Gruyter Oldenbourg (2014), 45–54.

  • 13.

    Jawaheer, G., Weller, P., and Kostkova, P. Modeling user preferences in recommender systems: A classification framework for explicit and implicit user feedback. ACM Trans. Interact. Intell. Syst. 4, 2 (2014), 8:1–8:26.

  • 14.

    Karimi, R., Freudenthaler, C., Nanopoulos, A., and Schmidt-Thieme, L. Exploiting the characteristics of matrix factorization for active learning in recommender systems. In Proc. RecSys ‘12, ACM (2012), 317–320.

  • 15.

    Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., and Newell, C. Explaining the user experience of recommender systems. User Mod. and User-Adapted Interaction, 22, 4–5 (2012), 441–504.

  • 16.

    Konstan, J. A., and Riedl, J. Recommender systems: From algorithms to user experience. User Mod. and User-Adapted Interaction 22, 1–2 (2012), 101–123.

  • 17.

    Koren, Y., Bell, R. M., and Volinsky, C. Matrix factorization techniques for recommender systems. IEEE Computer 42, 8 (2009), 30–37. [Crossref]

  • 18.

    Kuhlthau, C. C. Inside the search process: Information seeking from the user’s perspective. J. Am. Soc. Inf. Sci. 42, 5 (1991), 361–371.

  • 19.

    Loepp, B., Herrmanny, K. and Ziegler, J. Blended recommending: Integrating interactive information filtering and algorithmic recommender techniques. In Proc. CHI ‘15, ACM (to appear).

  • 20.

    Loepp, B., Hussein, T., and Ziegler, J. Choice-based preference elicitation for collaborative filtering recommender systems. In Proc. CHI ‘14, ACM (2014), 3085–3094.

  • 21.

    Mandl M. and Felfernig, A. Improving the Performance of Unit Critiquing. In Proc. UMAP ’12, Springer (2012), 176–187.

  • 22.

    Marchionini, G. Information Seeking in Electronic Environments. Cambridge University Press, 1995.

  • 23.

    McNee, S. M., Riedl, J. and Konstan, J. A. Being accurate is not enough: How accuracy metrics have hurt recommender systems. In Ext. Abstracts CHI ‘06, ACM (2006), 1097–1101.

  • 24.

    McNee, S. M., Riedl, J. and Konstan, J. A. Making recommendations better: An analytic model for human-recommender interaction. In Ext. Abstracts CHI ‘06, ACM (2006), 1103–1108.

  • 25.

    Moshagen, M. and Thielsch, M. T. Facets of visual aesthetics. Int. J. Hum.-Comput. St. 68, 10 (2010), 689–709.

  • 26.

    Pariser, E. The Filter Bubble: What the Internet is Hiding From You. Penguin Press, 2011.

  • 27.

    Parra, D., Brusilovsky, P., and Trattner, C. See what you want to see: Visual user-driven approach for hybrid recommendation. In Proc. IUI ‘14, ACM (2014), 235–240.

  • 28.

    Pu, P., Chen, L., and Hu, R. A user-centric evaluation framework for recommender systems. In Proc. RecSys ‘11, ACM (2011), 157–164.

  • 29.

    Pu, P., Chen, L., and Hu, R. Evaluating recommender systems from the users perspective: Survey of the state of the art. User Mod. and User-Adapted Interaction 22, 4–5 (2012), 317–355.

  • 30.

    Pu, P., Faltings, B., Chen, L., Zhang, J., and Viappiani, P. Recommender Systems Handbook. Springer, 2010, ch. Usability Guidelines for Product Recommenders Based on Example Critiquing Research, 511–545. [Web of Science]

  • 31.

    Ricci, F., Rokach, L., and Shapira, B. Recommender Systems Handbook. Springer, 2010, ch. Introduction to Recommender Systems Handbook, 1–35.

  • 32.

    Smyth, B., and McGinty, L. An analysis of feedback strategies in conversational recommenders. In Proc. AICS ‘03 (2003).

  • 33.

    Sacco, G. M., and Tzitzikas, Y. Dynamic Taxonomies and Faceted Search. Springer, 2009. [Web of Science]

  • 34.

    Salton, G., and Buckley, C. Improving retrieval performance by relevance feedback. In Readings in Information Retrieval. Morgan Kaufmann, 1997, 355–364.

  • 35.

    Thai, V., Rouille, P.-Y., and Handschuh, S. Visual abstraction and ordering in faceted browsing of text collections. ACM Trans. Intell. Syst. Technol. 3, 2 (2012), 21:1–21:24. [Web of Science]

  • 36.

    Tintarev, N., and Masthoff, J. Recommender Systems Handbook. Springer, 2010, ch. Designing and Evaluating Explanations for Recommender Systems, 479–510.

  • 37.

    Tvarožek, M., Barla, M., Frivolt, G., Tomša, M., and Bieliková, M. Improving semantic search via integrated personalized faceted and visual graph navigation. In Proc. SOFSEM ’08, Springer (2008), 778–789.

  • 38.

    Vig, J., Sen, S., and Riedl, J. Navigating the tag genome. In Proc. IUI ‘11, ACM (2011), 93–102.

  • 39.

    Voigt, M., Werstler, A., Polowinski, J., and Meißner, K. Weighted faceted browsing for characteristics-based visualization selection through end users. In Proc. EICS ’12, ACM (2012), 151–156.

  • 40.

    Xiao, B., and Benbasat, I. E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly 31, 1 (2007), 137–209.

  • 41.

    Yee, K.-P., Swearingen, K., Li, K. and Hearst, M. Faceted metadata for image search and browsing. In Proc. CHI ‘03, ACM (2003), 401–408.

  • 42.

    Zadeh, L. Fuzzy sets. Information and Control, 8, 3 (1965), 338–353.

  • 43.

    Zhang, J., Jones, N., and Pu, P. A visual interface for critiquing-based recommender systems. In Proc. EC ‘08, ACM (2008), 230–239.

  • 44.

    Zhao, X., Zhang, W., and Wang, J. Interactive collaborative filtering. In Proc. CIKM ‘13, ACM (2013), 1411–1420.

About the article

Benedikt Loepp

Benedikt Loepp, M. Sc. works as a researcher in the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. His research focuses on the field of recommender systems, in particular, new ways to increase their interactivity.

Katja Herrmanny

Katja Herrmanny, B. Sc. joined the Interactive Systems group as a student assistant in 2012, while studying in the bachelor program of Applied Cognitive and Media Science at the University of Duisburg-Essen. After receiving her bachelor’s degree in September 2012, she now works as a researcher, while studying the master program of Applied Cognitive and Media Science.

Prof. Dr.-Ing. Jürgen Ziegler

Jürgen Ziegler is a full professor in the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen where he directs the Interactive Systems Research Group. Prior to joining the University, he was head of the Competence Center for Software Technology and Interactive Systems at the Fraunhofer Institute for Industrial Engineering in Stuttgart.


Published Online: 2015-04-01

Published in Print: 2015-04-15



Citation Information: icom, ISSN (Online) 2196-6826, ISSN (Print) 1618-162X, DOI: https://doi.org/10.1515/icom-2015-0006. Export Citation

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