GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
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
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
IEEE Transactions on Knowledge and Data Engineering
An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Semantically enabled exploratory video search
Proceedings of the 3rd International Semantic Search Workshop
Towards exploratory video search using linked data
Multimedia Tools and Applications
Ontology driven bee's foraging approach based self adaptive online recommendation system
Journal of Systems and Software
Using link semantics to recommend collaborations in academic social networks
Proceedings of the 22nd international conference on World Wide Web companion
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Much early evaluation work focused specifically on the "accuracy" of recommendation algorithms. Good recommendation (in terms of accuracy) has, however, to be coupled with other considerations. This work suggests measures aiming at evaluating other aspects than accuracy of recommendation algorithms. Other considerations include (1) coverage, which measures the percentage of a data set that a recommender system is able to provide recommendation for, (2) confidence metrics that can help users make more effective decisions, (3) computing time, which measures how quickly an algorithm can produce good recommendations, (4) novelty/serendipity, which measure whether a recommendation is original, and (5) robustness which measure the ability of the algorithm to make good predictions in the presence of noisy or sparse data. Six collaborative recommendation methods are investigated. Results on artificial data sets (for robustness) or on the real MovieLens data set (for accuracy, novelty, and computing time) are included and analyzed, showingthat kernel-based algorithms provide the best results overall.