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Report on RecSys 2015 Workshop on New Trends in Content-Based Recommender Systems

Published:29 January 2016Publication History
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Abstract

This article reports on the CBRecSys 2015 workshop, the second edition of the workshop on new trends in content-based recommender systems, co-located with RecSys 2015 in Vienna, Austria. Content-based recommendation has been applied successfully in many different domains, but it has not seen the same level of attention as collaborative filtering techniques have. Nevertheless, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. The CBRecSys workshop series provides a dedicated venue for work dedicated to all aspects of content-based recommender systems.

References

  1. C. Benkoussas and P. Bellot. Cross-Document Search Engine For Book Recommendation. In CBRecSys '15: Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender, pages 42--49, 2015.Google ScholarGoogle Scholar
  2. L. Bernardi, J. Kamps, J. Kiseleva, and M. J. I. Müller. The Continuous Cold-start Problem in e-Commerce Recommender Systems. In CBRecSys '15: Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender, pages 30--33, 2015.Google ScholarGoogle Scholar
  3. T. Bogers, M. Koolen, and I. Cantador. Workshop on New Trends in Content-based Recommender Systems: (CBRecSys 2014). In RecSys '14: Proceedings of the Eighth ACM Conference on Recommender Systems, pages 379--380, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. T. Bogers, M. Koolen, and I. Cantador. Report on RecSys 2014: Workshop on New Trends in Content-Based Recommender Systems. SIGIR Forum, 49(1):20--26, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. V. de Graaff, A. van de Venis, M. van Keulen, and R. A. de By. Generic knowledge-based Analysis of Social Media for Recommendations. In CBRecSys '15: Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender, pages 22--29, 2015.Google ScholarGoogle Scholar
  6. G. Dror, N. Koenigstein, Y. Koren, and M. Weimer. The Yahoo! Music Dataset and KDDCup'11. In JMLR Workshop and Conference Proceedings, volume 18 of Proceedings of KDD Cup 2011, pages 3--18, 2012.Google ScholarGoogle Scholar
  7. M. Koolen, T. Bogers, A. van den Bosch, and J. Kamps. Looking for books in social media: An analysis of complex search requests. In Advances in Information Retrieval: 37th European Conference on IR Research (ECIR 2015), LNCS. Springer, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  8. M. Kula. Metadata Embeddings for User and Item Cold-start Recommendations. In CBRec- Sys '15: Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender, pages 14--21, 2015.Google ScholarGoogle Scholar
  9. K. Labille, S. Gauch, and A. S. Joseph. Conceptual Impact-Based Recommender System for CiteSeerx. In CBRecSys '15: Proceedings of the 2nd Workshop on New Trends on Content- Based Recommender, pages 50--53, 2015.Google ScholarGoogle Scholar
  10. P. Lops, M. de Gemmis, and G. Semeraro. Content-based Recommender Systems: State of the Art and Trends. In Recommender Systems Handbook, pages 73--105. 2011.Google ScholarGoogle ScholarCross RefCross Ref
  11. C. Musto, P. Basile, M. de Gemmis, P. Lops, G. Semeraro, and S. Rutigliano. Automatic Selection of Linked Open Data Features in Graph-based Recommender Systems. In CBRecSys'15: Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender, pages 10--13, 2015.Google ScholarGoogle Scholar
  12. I. Pilászy and D. Tikk. Recommending New Movies: Even a Few Ratings Are More Valuable Than Metadata. In RecSys '09: Proceedings of the Third ACM Conference on RecommenderSystems, pages 93--100. ACM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. Poussevin, V. Guigue, and P. Gallinari. Extended Recommendation Framework: Generating the Text of a User Review as a Personalized Summary. In CBRecSys '15: Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender, pages 34--41, 2015.Google ScholarGoogle Scholar
  14. P. Tomeo, T. D. Noia, M. de Gemmis, P. Lops, G. Semeraro, and E. D. Sciascio. Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity. In CBRecSys'15: Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender, pages 2--9, 2015.Google ScholarGoogle Scholar

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      • Published in

        cover image ACM SIGIR Forum
        ACM SIGIR Forum  Volume 49, Issue 2
        December 2015
        141 pages
        ISSN:0163-5840
        DOI:10.1145/2888422
        Issue’s Table of Contents

        Copyright © 2016 Authors

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 29 January 2016

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