Recommender Systems
Recommender Systems
Tamara Heck
Recommendation systems suggest relevant and suitable resources (such as products, music, people and tags) to a user. The goal is to predict a user’s preferences. The prediction is based on resource information (content-based) and information about users and users' social environment (collaborative filtering). Recommender services are used in many different fields, e.g. in e-commerce, social bookmarking services and music websites. The can also be used in the academic field, e.g. to suggest collaborators to target researchers, based on social infromation like publications, citations and bookmarks.
Publications
Jasberg, Kevin and Sergej Sizov (2018). “Human Uncertainty in Explicit User Feedback and its Impact on Data Analytics, Machine Learning and User-Related Reasoning”. Behaviour & Information Technology, no pages yet.
Jasberg, Kevin and Sergej Sizov (2018). “Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Recommendation and Personalisation”. In Proceedings of the Conference on Web Information Systems Engineering (WISE) , no pages yet.
Jasberg, Kevin and Sergej Sizov (2018). “Human Uncertainty and Ranking Error - Fallacies in Metric-Based Evaluation of Recommender Systems”. In Proceedings of the ACM/SIGAPP Symposium On Applied Computing(SAC). ACM, pp. 1358-1365
Jasberg, Kevin and Sergej Sizov (2018). “Dealing with Uncertainties in User Feedback: Strategies Between Denying and Accepting”. arXiv:1802.05895.
Jasberg, Kevin and Sergej Sizov (2018). “Menschliche Unsicherheit in Empfehlungssystemen: Was wir von den klassischen Naturwissenschaften übernehmen können”. Information — Wissenschaft & Praxis, 69.1, pp. 21–30.
Jasberg, Kevin and Sergej Sizov (2017). “Assessment of Prediction Techniques: The Impact of Human Uncertainty”. In Proceedings of the Conference on Web Information Systems Engineering (WISE). Springer, pp. 106–120.
Jasberg, Kevin and Sergej Sizov (2017). “Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining”. In Proceedings of the Conference on User Modeling, Adaptation and Personalization (UMAP). ACM, pp. 104–112.
Jasberg, Kevin and Sergej Sizov (2017). “The Magic Barrier Revisited: Accessing Natural Limitations of Recommender Assessment”. In Proceedings of the Conference on Recommender Systems (RecSys). ACM, pp. 56–64
Jasberg, Kevin and Sergej Sizov (2017). “Re-Evaluating the Netflix Prize - Human Uncertainty and its Impacton Reliability”. arXiv:1706.08866.
Heck, T. & Schaer, P. (2013). Performing Informetric Analysis on Information Retrieval Test Collections: Preliminary Experiments in the Physics Domain. In Proceedings of ISSI 2013 - 14th International Society of Scientometrics and Informetrics Conference (pp. 1392-1400).
Heck, T. (2013) Combining Social Information for Academic Networking. In Proceedings of CSCW '13, Conference on Computer supported cooperative work. New York, NY: ACM, 1387-1398.
Heck, T. (2012). Analyse von sozialen Informationen für Autorenempfehlungen. Information. Wissenschaft & Praxis, 63(4), 261–272. doi: 10.1515/iwp-2012-0048
Heck, T. (2012). Recommendation for Social Networking in Academia. In Gunilla Widén and Kim Holmberg (eds.), Library and Information Science Volume 5: Social Information Research (pp. 237-265), London: Emerald Group Pub.
Heck, T. (2012). Analyse von Folksonomy basierten Netzwerken als komplementärer Ansatz für Autorenempfehlungen in der Wissenschaft. In Proceedings of the 2. DGI 2012 Conference: Social Media und Web Science - Das Web als Lebensraum (pp. 179-193). Frankfurt a.M.: DGI.
Peters, I., Kipp, M.E.I., Heck, T., Gwizdka, J., Lu, K., Neal, D.R., Spiteri, L. (2011). Social Tagging & Folksonomies: Indexing, Retrieving...and Beyond? In Proceedings of the Annual Meeting of the American Society for Information Science and Technology, New Orleans, October 9-13, 2011.
Heck, T., Peters, I., & Stock, W.G. (2011). Testing collaborative filtering against co-citation analysis and bibliographic coupling for academic author recommendation. In ACM RecSys’11. 3rd Workshop on Recommender Systems and the Social Web, Oct. 23, Chicago, Il.
Heck, T., Hanraths, O., & Stock, W.G. (2011). Expert recommendation for knowledge management in academia. In Proceedings of the Annual Meeting of the American Society for Information Science and Technology, New Orleans, October 9-13, 2011.
Heck, T. (2011). A comparison of different user-similarity measures as basis for research and scientific cooperation. In Proceedings of Issome '11, Information Science and Social Media - International Conference, Åbo/Turku, Finnland, August 24-26, 2011.
Heck, T., Peters, I. (2010). Experten-Empfehlungen mit Social Bookmarking-Services. In: i-com -Zeitschrift für interaktive und kooperative Medien, 9 (3), 7-11. Link zur Zeitschrift: http://i-com-media.de
Heck, T., Peters, I. (2010). Implizite Digitale Soziale Netze als Basis für Expertenempfehlungssysteme. In K.-P. Fähnrich und B. Franczyk (Eds.) Informatik 2010, Service Science – Neue Perspektiven für die Informatik (1), pp. 613-618.
Heck, T., & Peters, I. (2010). Expert Recommender Systems: Establishing Communities of Practice Based on Social Bookmarking Systems. In Proceedings of I-Know 2010. 10th International Conference on Knowledge Management and Knowledge Technologies, pp. 458-464.