An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Evaluating product search and recommender systems for E-commerce environments
Electronic Commerce Research
From Web to Social Web: Discovering and Deploying User and Content Profiles
User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce
Proceedings of the 19th international conference on World wide web
Eye-tracking product recommenders' usage
Proceedings of the fourth ACM conference on Recommender systems
Do clicks measure recommendation relevancy?: an empirical user study
Proceedings of the fourth ACM conference on Recommender systems
Recommender Systems Handbook
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
PEN recsys: a personalized news recommender systems framework
Proceedings of the 2013 International News Recommender Systems Workshop and Challenge
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In this paper we introduce RecLab, a system designed to enable developers to build and test recommendation algorithms for eCommerce websites. RecLab supports a variety of context and feedback that recommenders can take advantage of to improve the quality of their recommendations. RecLab is unique in that recommenders built on top of RecLab APIs can run in the RichRelevance cloud environment in addition to working with offline data sets. This environment provides on-site recommendations to some of the largest eCommerce sites in existence. By running in the cloud, we are able to avoid the pitfalls that have historically made it difficult for researchers to work with real data and live traffic. We bring code to data, rather than bringing sensitive merchant data to code running outside the cloud. Peer-reviewed recommenders built with RecLab will be chosen to run in the cloud on live data, given their authors unprecedented feedback into their performance in situ.