Abstract
Most user-centric studies of information access systems in literature suffer from unrealistic settings or limited numbers of users who participate in the study. In order to address this issue, the idea of a living lab has been promoted. Living labs allow us to evaluate research hypotheses using a large number of users who satisfy their information need in a real context. In this paper, we introduce a living lab on news recommendation in real time. The living lab has first been organized as News Recommendation Challenge at ACM RecSys’13 and then as campaign-style evaluation lab NEWSREEL at CLEF’14. Within this lab, researchers were asked to provide news article recommendations to millions of users in real time. Different from user studies which have been performed in a laboratory, these users are following their own agenda. Consequently, laboratory bias on their behavior can be neglected. We outline the living lab scenario and the experimental setup of the two benchmarking events. We argue that the living lab can serve as reference point for the implementation of living labs for the evaluation of information access systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Adomavicius, G., Kwon, Y.O.: Improving aggregate recommendation diversity using ranking-based techniques. Knowledge and Data Engineering 24(5), 896–911 (2012)
Allan, J.: Hard track overview in trec 2003: High accuracy retrieval from documents. In: TREC, pp. 24–37 (2003)
Amatriain, X.: Mining large streams of user data for personalized recommendations. ACM SIGKDD Explorations Newsletter 14(2), 37 (2013)
Azzopardi, L., Balog, K.: Towards a living lab for information retrieval research and development. In: Forner, P., Gonzalo, J., Kekäläinen, J., Lalmas, M., de Rijke, M. (eds.) CLEF 2011. LNCS, vol. 6941, pp. 26–37. Springer, Heidelberg (2011)
Balog, K., Elsweiler, D., Kanoulas, E., Kelly, L., Smucker, M.: Report on the cikm workshop on living labs for information retrieval evaluation. SIGIR Forum 48(1) (2014)
Belkin, N.J.: Some(what) grand challenges for information retrieval. In: ECIR, p. 1 (2008)
Bennett, J., Lanning, S.: The netflix prize. In: KDDCup (2007)
Brodt, T., Hopfgartner, F.: Shedding Light on a Living Lab: The CLEF NEWSREEL Open Recommendation Platform. In: Proceedings of the Information Interaction in Context Conference, IIiX 2014. Springer (to appear, 2014)
Cleverdon, C., Mills, J., Keen, M.: Factors determining the performance of indexing systems. Technical report, ASLIB Cranfield project, Cranfield (1966)
Clough, P., Sanderson, M.: Evaluating the performance of information retrieval systems using test collections. Information Research 18(2) (2013)
Dror, G., Koenigstein, N., Koren, Y., Weimer, M.: The Yahoo! Music Dataset and KDD-Cup. In: JMLR: Workshop and Conference Proceedings, pp. 3–18 (2012)
Dumais, S., Belkin, N.: The trec interactive tracks: Putting the user into search. In: TREC (2005)
Esiyok, C., Kille, B., Jain, B.J., Hopfgartner, F., Albayrak, S.: Users’ reading habits in online news portals. In: IIiX 2014: Proceedings of Information Interaction in Context Conference. ACM (to appear, August 2014)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999, pp. 230–237. ACM (1999)
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)
Hopfgartner, F., Jose, J.M.: Semantic user profiling techniques for personalised multimedia recommendation. Multimedia Syst. 16(4-5), 255–274 (2010)
Ivory, M.Y., Hearst, M.A.: The state of the art in automating usability evaluation of user interfaces. ACM Comput. Surv. 33(4), 470–516 (2001)
Kamps, J., Geva, S., Peters, C., Sakai, T., Trotman, A., Voorhees, E.M.: Report on the sigir 2009 workshop on the future of ir evaluation. SIGIR Forum 43(2), 13–23 (2009)
Kelly, D., Dumais, S.T., Pedersen, J.O.: Evaluation challenges and directions for information-seeking support systems. IEEE Computer 42(3), 60–66 (2009)
Kille, B., Hopfgartner, F., Brodt, T., Heintz, T.: The plista dataset. In: NRS 2013: Proceedings of the International Workshop and Challenge on News Recommender Systems, pp. 14–21. ACM (2013)
Konstan, J., Riedl, J.: Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction 22(1-2), 101–123 (2012)
Lommatzsch, A.: Real-time news recommendation using context-aware ensembles. In: de Rijke, M., Kenter, T., de Vries, A.P., Zhai, C., de Jong, F., Radinsky, K., Hofmann, K. (eds.) ECIR 2014. LNCS, vol. 8416, pp. 51–62. Springer, Heidelberg (2014)
Lommatzsch, A., Plumbaum, T., Albayrak, S.: A linked dataverse knows better: Boosting recommendation quality using semantic knowledge. In: Proc. of the 5th Intl. Conf. on Advances in Semantic Processing, Wilmington, DE, USA, pp. 97–103. IARIA (2011)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Phelan, O., McCarthy, K., Smyth, B.: Using twitter to recommend real-time topical news. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys 2009, pp. 385–388. ACM, New York (2009)
Pirolli, P.: Powers of 10: Modeling complex information-seeking systems at multiple scales. IEEE Computer 42(3), 33–40 (2009)
Said, A., Lin, J., Bellogín, A., de Vries, A.: A month in the life of a production news recommender system. In: Proceedings of the 2013 Workshop on Living Labs for Information Retrieval Evaluation, LivingLab 2013, pp. 7–10. ACM (2013)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Recommender Systems Handbook, pp. 257–297. Springer (2011)
Tavakolifard, M., Gulla, J.A., Almeroth, K.C., Hopfgartner, F., Kille, B., Plumbaum, T., Lommatzsch, A., Brodt, T., Bucko, A., Heintz, T.: Workshop and challenge on news recommender systems. In: RecSys 2013: Proceedings of the International ACM Conference on Recommender Systems. ACM (October 2013)
TNS Opinion & Social. Special Eurobarometer 386 – Europeans and their Languages. Technical report, European Commission (2012)
Vallet, D., Hopfgartner, F., Jose, J.: Use of implicit graph for recommending relevant videos: a simulated evaluation. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 199–210. Springer, Heidelberg (2008)
Voorhees, E.M., Harman, D.K.: TREC: Experiment and Evaluation in Information Retrieval, 1st edn. MIT Press, Cambridge (2005)
Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW 2005, pp. 22–32. ACM (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Hopfgartner, F., Kille, B., Lommatzsch, A., Plumbaum, T., Brodt, T., Heintz, T. (2014). Benchmarking News Recommendations in a Living Lab. In: Kanoulas, E., et al. Information Access Evaluation. Multilinguality, Multimodality, and Interaction. CLEF 2014. Lecture Notes in Computer Science, vol 8685. Springer, Cham. https://doi.org/10.1007/978-3-319-11382-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-11382-1_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11381-4
Online ISBN: 978-3-319-11382-1
eBook Packages: Computer ScienceComputer Science (R0)