GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Evaluating the dynamic properties of recommendation algorithms
Proceedings of the fourth ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
RecDB: towards DBMS support for online recommender systems
PhD '12 Proceedings of the on SIGMOD/PODS 2012 PhD Symposium
Proceedings of the sixth ACM conference on Recommender systems
Scalable similarity-based neighborhood methods with MapReduce
Proceedings of the sixth ACM conference on Recommender systems
When recommenders fail: predicting recommender failure for algorithm selection and combination
Proceedings of the sixth ACM conference on Recommender systems
An online recommendation system for e-commerce based on apache mahout framework
Proceedings of the 2013 annual conference on Computers and people research
Rating support interfaces to improve user experience and recommender accuracy
Proceedings of the 7th ACM conference on Recommender systems
Dynamic generation of personalized hybrid recommender systems
Proceedings of the 7th ACM conference on Recommender systems
PEN recsys: a personalized news recommender systems framework
Proceedings of the 2013 International News Recommender Systems Workshop and Challenge
Toward identification and adoption of best practices in algorithmic recommender systems research
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
Teaching recommender systems at large scale: evaluation and lessons learned from a hybrid MOOC
Proceedings of the first ACM conference on Learning @ scale conference
Hybreed: A software framework for developing context-aware hybrid recommender systems
User Modeling and User-Adapted Interaction
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Recommender systems research is being slowed by the difficulty of replicating and comparing research results. Published research uses various experimental methodologies and metrics that are difficult to compare. It also often fails to sufficiently document the details of proposed algorithms or the evaluations employed. Researchers waste time reimplementing well-known algorithms, and the new implementations may miss key details from the original algorithm or its subsequent refinements. When proposing new algorithms, researchers should compare them against finely-tuned implementations of the leading prior algorithms using state-of-the-art evaluation methodologies. With few exceptions, published algorithmic improvements in our field should be accompanied by working code in a standard framework, including test harnesses to reproduce the described results. To that end, we present the design and freely distributable source code of LensKit, a flexible platform for reproducible recommender systems research. LensKit provides carefully tuned implementations of the leading collaborative filtering algorithms, APIs for common recommender system use cases, and an evaluation framework for performing reproducible offline evaluations of algorithms. We demonstrate the utility of LensKit by replicating and extending a set of prior comparative studies of recommender algorithms --- showing limitations in some of the original results --- and by investigating a question recently raised by a leader in the recommender systems community on problems with error-based prediction evaluation.