GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparing State-of-the-Art Collaborative Filtering Systems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Expert Systems with Applications: An International Journal
Towards text-based recommendations
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Government-to-business personalized e-services using semantic-enhanced recommender system
EGOVIS'11 Proceedings of the Second international conference on Electronic government and the information systems perspective
Distance metrics for high dimensional nearest neighborhood recovery: Compression and normalization
Information Sciences: an International Journal
Reperio: A Generic and Flexible Industrial Recommender System
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
A trust-semantic fusion-based recommendation approach for e-business applications
Decision Support Systems
Geographical and temporal similarity measurement in location-based social networks
Proceedings of the Second ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
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The aim of collaborative filteringis to help usersto find itemsthat they should appreciate from huge catalogues. In that field, we can distinguish user-basedfrom item-basedapproaches. The former is based on the notion of user neighbourhoods while the latter uses item neighbourhoods.The definition of similaritybetween users and items is a key problem in both approaches. While traditional similarity measures can be used, we will see in this paper that bespoke ones, that are tailored to type of data that is typically available (i.e. very sparse), tend to lead to better results.Extensive experiments are conducted on two publicly available datasets, called MovieLensand Netflix. Many similarity measures are compared. And we will show that using weighted similarity measures significantly improves the results of both user- and item-based approaches.