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ACM International Conference Proceedings Series (ICPS), pp. In: Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference (RecSys).

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doi: 10.1145/2532508.2532512īeel, J., Langer, S., Genzmehr, M., Gipp, B., Nürnberger, A.: A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation. Malta, Valletta (2013)īeel, J., Langer, S., Genzmehr, M., Gipp, B., Breitinger, C., Nürnberger, A.: Research paper recommender system evaluation: a quantitative literature survey. (eds.) Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), pp. In: Aalberg, T., Dobreva, M., Papatheodorou, C., Tsakonas, G., Farrugia, C. doi: 10.1145/1998076.1998188īeel, J., Langer, S., Genzmehr, M.: Sponsored versus organic (Research Paper) recommendations and the impact of labeling. In: Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries (JCDL). Springer, Glasgow (2010)īeel, J., Gipp, B., Langer, S., Genzmehr, M.: Docear: an academic literature suite for searching, organizing and creating academic literature. Lecture Notes of Computer Science (LNCS), pp. Proceedings of the 14th European Conference on Digital Libraries (ECDL’10). (eds.) Research and Advanced Technology for Digital Libraries. In Lalmas, M., Jose, J., Rauber, A., Sebastiani, F., Frommholz, I.

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doi: 10.1007/978-2-8_12īeel, J., Gipp, B., Shaker, A., Friedrich, N.: SciPlore Xtract: extracting titles from scientific PDF documents by analyzing style information (font size). (eds.) Proceedings of the 19th International Conference on Theory and Practice of Digital Libraries (TPDL). In: Kapidakis, S., Mazurek, C., Werla, M. Otto-von-Guericke Universität Magdeburg (2015)īeel, J., Langer, S.: A comparison of offline evaluations, online evaluations, and user studies in the context of research-paper recommender systems. Springer, Berlin (2009)īeel, J.: Towards effective research-paper recommender systems and user modeling based on mind maps. (eds.) User Modeling, Adaptation, and Personalization, pp. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. i like it not: Evaluating user ratings noise in recommender systems. ACM, New York (2007)Īmatriain, X., Pujol, J., Oliver, N.: I like it. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. We discuss these findings and conclude that to ensure reproducibility, the recommender-system community needs to (1) survey other research fields and learn from them, (2) find a common understanding of reproducibility, (3) identify and understand the determinants that affect reproducibility, (4) conduct more comprehensive experiments, (5) modernize publication practices, (6) foster the development and use of recommendation frameworks, and (7) establish best-practice guidelines for recommender-systems research.Īl-Maskari, A., Sanderson, M., Clough, P.: The relationship between IR effectiveness measures and user satisfaction. Since minor variations in approaches and scenarios can lead to significant changes in a recommendation approach’s performance, ensuring reproducibility of experimental results is difficult. For instance, the optimal size of an algorithms’ user model depended on users’ age. Some of the determinants have interdependencies. Determinants we examined include user characteristics (gender and age), datasets, weighting schemes, the time at which recommendations were shown, and user-model size. We found several determinants that may contribute to the large discrepancies observed in recommendation effectiveness. For example, in one news-recommendation scenario, the performance of a content-based filtering approach was twice as high as the second-best approach, while in another scenario the same content-based filtering approach was the worst performing approach. The experiments show that there are large discrepancies in the effectiveness of identical recommendation approaches in only slightly different scenarios, as well as large discrepancies for slightly different approaches in identical scenarios. We conduct experiments using Plista’s news recommender system, and Docear’s research-paper recommender system. In this article, we examine the challenge of reproducibility in recommender-system research. However, comparing their effectiveness is a challenging task because evaluation results are rarely reproducible. Numerous recommendation approaches are in use today.