Paper-Conference

Information retrieval and user-centric recommender system evaluation
Traditional recommender system evaluation focuses on raising the accuracy, or lowering the rating prediction error of the …
Alan Said, A. Bellogín, A. De Vries, B. Kille
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News Recommendation in the Wild: CWI's Recommendation Algorithms in the NRS type: publication profile: false Challenge
This work presents the recommendation algorithms deployed by the winning team (recomenders.net) in the ACM RecSys 2013 News Recommender …
Alan Said, Alejandro Bellogín, Arjen De Vries
Jan 1, 2013
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The 3rd workshop on context-awareness in retrieval and recommendation (CaRR 2013)
M. Böhmer, E.W. De Luca, Alan Said, J. Teevan
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2nd workshop on context-awareness in retrieval and recommendation
Context-aware information is widely available in various ways and is becoming more and more important for enhancing retrieval …
E.W. De Luca, M. Böhmer, Alan Said, E. Chi
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Analyzing weighting schemes in collaborative filtering: Cold start, post cold type: publication profile: false start and power users
Collaborative filtering recommender systems provide their users with relevant items based on information from other similar users. …
Alan Said, B.J. Jain, S. Albayrak
Jan 1, 2012
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Correlating perception-oriented aspects in user-centric recommender system
Research on recommender systems evaluation generally measures the quality of the algorithm, or system, offline, i.e. based on some …
Alan Said, B.J. Jain, A. Lommatzsch, S. Albayrak
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Estimating the magic barrier of recommender systems: A user study
Recommender systems are commonly evaluated by trying to predict known, withheld, ratings for a set of users. Measures such as the …
Alan Said, B.J. Jain, S. Narr, T. Plumbaum, S. Albayrak, C. Scheel
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KMulE: A framework for user-based comparison of recommender algorithms
Collaborative Filtering Recommender Systems come in a wide variety of variants. In this paper we present a system for visualizing and …
Alan Said, E.W. De Luca, B. Kille, B. Jain, I. Micus, S. Albayrak
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Recommender systems challenge 2012
The Recommender System Challenge 2012 invited participants to work on two tracks with real-world datasets and to submit their …
N. Manouselis, J. Hermanns, Alan Said, B. Kille, D. Tikk, H. Drachsler, K. Verbert, K. Jack
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Recommender systems evaluation: A 3D benchmark
Recommender systems add value to vast content resources by matching users with items of interest. In recent years, immense progress has …
Alan Said, D. Tikk, Y. Shi, M. Larson, K. Stumpf, P. Cremonesi
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