Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering
Proceedings of the 2007 ACM conference on Recommender systems
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
The effect of sparsity on collaborative filtering metrics
ADC '09 Proceedings of the Twentieth Australasian Conference on Australasian Database - Volume 92
e-learning experience using recommender systems
Proceedings of the 42nd ACM technical symposium on Computer science education
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
Expert Systems with Applications: An International Journal
Collaborative filtering based on significances
Information Sciences: an International Journal
Interest-based real-time content recommendation in online social communities
Knowledge-Based Systems
A collaborative filtering similarity measure based on singularities
Information Processing and Management: an International Journal
A recommendation algorithm combining clustering method and slope one scheme
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
A framework for collaborative filtering recommender systems
Expert Systems with Applications: An International Journal
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Collaborative Filtering, one of the most widely used approach in Recommender System, predicts a user's rating towards an item by aggregating ratings given by users having similar preference to that user. In existing approaches, user similarity is often computed on the whole set of items. However, because the number of items is often very large, and so is the diversity among items, users who have similar preference in one category of items may have totally different judgement on items of another kind. In order to deal with this problem, we propose a method of clustering items, so that inside a cluster, similarity between users does not change significantly. After that, when predicting rating of a user towards an item, we only aggregate ratings of users who have high similarity degree with that user inside the cluster to which that item belongs. Experiments evaluating our approach are carried out on the real dataset taken from movies recommendation system of MovieLens web site. Preliminary results suggest that our approach can improve prediction accuracy compared to existing approaches.