Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
A Recommendation Algorithm Using Multi-Level Association Rules
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Recency-based collaborative filtering
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Improving maximum margin matrix factorization
Machine Learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
IEEE Transactions on Knowledge and Data Engineering
Exploiting contextual information in recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
An empirical study on effectiveness of temporal information as implicit ratings
Expert Systems with Applications: An International Journal
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Communications of the ACM
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Alpha galois lattices: an overview
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
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Collaborative filtering has been extensively studied in the context of ratings prediction. However, industrial recommender systems often aim at predicting a few items of immediate interest to the user, typically products that (s)he is likely to buy in the near future. In a collaborative filtering setting, the prediction may be based on the user's purchase history rather than rating information, which may be unreliable or unavailable. In this paper, we present an experimental evaluation of various collaborative filtering algorithms on a real-world dataset of purchase history from customers in a store of a French home improvement and building supplies chain. These experiments are part of the development of a prototype recommender system for salespeople in the store. We show how different settings for training and applying the models, as well as the introduction of domain knowledge may dramatically influence both the absolute and the relative performances of the different algorithms. To the best of our knowledge, the influence of these parameters on the quality of the predictions of recommender systems has rarely been reported in the literature.