Measuring retrieval effectiveness based on user preference of documents
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CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
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Machine Learning - Special issue on multistrategy learning
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An algorithmic framework for performing collaborative filtering
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User Modeling and User-Adapted Interaction
Eigentaste: A Constant Time Collaborative Filtering Algorithm
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User Modeling and User-Adapted Interaction
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
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ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Evaluating the intrusion cost of recommending in recommender systems
UM'05 Proceedings of the 10th international conference on User Modeling
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
User-based Collaborative Filtering: Sparsity and Performance
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Expert Systems with Applications: An International Journal
Collaborative filtering based on significances
Information Sciences: an International Journal
Extended precision quality measure for recommender systems
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
A new cross-validation technique to evaluate quality of recommender systems
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
A framework for collaborative filtering recommender systems
Expert Systems with Applications: An International Journal
RESYGEN: A Recommendation System Generator using domain-based heuristics
Expert Systems with Applications: An International Journal
A balanced memory-based collaborative filtering similarity measure
International Journal of Intelligent Systems
Integrating multiple experts for correction process in interactive recommendation systems
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Information Processing and Management: an International Journal
Improving collaborative filtering-based recommender systems results using Pareto dominance
Information Sciences: an International Journal
Knowledge-Based Systems
| Hi-index | 12.06 |
It is difficult to deny that comparison between recommender systems requires a common way for evaluating them. Nevertheless, at present, they have been evaluated in many, often incompatible, ways. We affirm this problem is mainly due to the lack of a common framework for recommender systems, a framework general enough so that we may include the whole range of recommender systems to date, but specific enough so that we can obtain solid results. In this paper, we propose such a framework, attempting to extract the essential features of recommender systems. In this framework, the most essential feature is the objective of the recommender system. What is more, in this paper, recommender systems are viewed as applications with the following essential objective. Recommender systems must: (i) choose which (of the items) should be shown to the user, (ii) decide when and how the recommendations must be shown. Next, we will show that a new metric emerges naturally from this framework. Finally, we will conclude by comparing the properties of this new metric with the traditional ones. Among other things, we will show that we may evaluate the whole range of recommender systems with this single metric.