Advertisement

Abstract

This paper describes the operation of and research behind a networked application for the delivery of personalised streams of music at Trinity College Dublin. Smart Radio is a web based client-server application that uses streaming audio technology and recommendation techniques to allow users build, manage and share music programmes. Since good content descriptors are difficult to obtain in the audio domain, we originally used automated collaborative filtering, a ‘content less’ approach as our recommendation strategy. We describe how we improve the ACF technique by leveraging a light content-based technique that attempts to capture the user’s current listening ‘context’. This involves a two stage retrieval process where ACF recommendations are ranked according to the user’s current interests. Finally, we demonstrate a novel on-line evaluation strategy that pits the ACF strategy against the context-boosted strategy in a real time competition.

Keywords

Recommender System User Profile Trinity College Case Representation Recommendation Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Allmusic (2002). E-mail correspondence on licensing arrangements for the access to the allmusic.com databaseGoogle Scholar
  2. 2.
    Balabanovic, M., Shoham, Y., Learning Information Retrieval Agents: Experiments with Automated Web Browsing, in AAAI Spring Symposium on Information Gathering, Stanford, CA, March 1995Google Scholar
  3. 3.
    Branting, L. K., Learning Feature Weights from Customer Return-Set Selections. The Journal of Knowledge and Information Systems (KAIS). To appear, 2003.Google Scholar
  4. 4.
    Hayes, C., Doyle, M., Cunningham, P., Distributed CBR Using XML, presented at the Workshop for Intelligent Systems and Electronic Commerce as part of the German Conference on Artificial Intelligence (KI-98) September 15–17 1998. also available as Trinity College Dublin Computer Science Report TCD-CS-1998-06.Google Scholar
  5. 5.
    Burke, R., Hybrid Recommender Systems: Surveys and Experiments in User Modelling and User-Adapted Interaction 12(4): 331–370; Nov 2002. Kluwer press.zbMATHCrossRefGoogle Scholar
  6. 6.
    Gentner, D., and Forbus, K. D., MAC/FAC: A model of similarity based access and mapping. In Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society. ErlbaumGoogle Scholar
  7. 7.
    Foote, J., an overview of Audio information retrieval. Multimedia Systems 7: 2–10 (1999). Springer VerlagCrossRefGoogle Scholar
  8. 8.
    Hayes, C., Cunningham, P., SmartRadio-community based music radio; Knowledge Based Systems, special issue ES2000, Volume 14, Issue3–4, June 2001, ElsevierGoogle Scholar
  9. 9.
    Hayes, C., Massa, P., Avesani, P., Cunningham, P., An on-line evaluation framework for recommender systems in the proceedings of the IWorkshop on Recommendation and Personalization Systems, AH 2002, Malaga, Spain, 2002. Springer Verlag.Google Scholar
  10. 10.
    Lenz, M., Case Retrieval Nets as a model for building flexible information systems. PhD dissertation, Humboldt University, Berlin. Faculty of Mathematics and Natural Sciences.Google Scholar
  11. 11.
    Lieberman H., Letiza: An Agent That Assists Web Browsing” in Proceedings of the International Joint Conference on Artificial Intelligence IJCAI-95.(Montreal 1995).Google Scholar
  12. 12.
    Lieberman, H., Fry, C., and Weitzman, L., Exploring the Web with Reconnaissance Agents,” Communications of the ACM, Vol. 44, No. 8, August 2001.Google Scholar
  13. 13.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J. An Open Architecture for Collaborative Filtering of Netnews. pages 175–186. ACM Conference on Computer Supported Co-operative Work, 1994.Google Scholar
  14. 14.
    Schafer, J.B., Konstan, J.A., and Riedl, J., Recommender Systems in E-Commerce. In ACM Conference on Electronic Commerce (EC-99), pages 158–166, 1999.Google Scholar
  15. 15.
    Schlit B. et al, Context-Aware Computing Applications, IEEE Workshop on Mobile Computing Systems and Applications 1994Google Scholar
  16. 16.
    Shardanand, U., and Mayes, P., Social Information Filtering: Algorithms for Automating ‘Word of Mouth’, in Proceedings of CHI95, 210–217, 1995.Google Scholar
  17. 17.
    Stahl, A., Learning Feature Weights from Case Order Feedback. Proceedings of the 4th International Conference on Case-Based Reasoning, ICCBR 2001Google Scholar
  18. 18.
    Swearingen, K., Sinha, R., Beyond Algorithms: An HCI Perspective on Recommender Systems, ACM SIGIR Workshop on Recommender Systems, 2001.Google Scholar

Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  • Conor Hayes
    • 1
  • Pádraig Cunningham
    • 1
  1. 1.Computer Science DepartmentTrinity CollegeDublinIreland

Personalised recommendations