Collaborative machine learning

Authors:
Thomas Hofmann;Justin Basilico
Affiliations:
Department of Computer Science, Brown University, Providence, RI;Department of Computer Science, Brown University, Providence, RI
Venue:
From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments
Year:
2005

Citing 14
Cited 2

Using collaborative filtering to weave an information tapestry

Communications of the ACM - Special issue on information filtering
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
Fab: content-based, collaborative recommendation

Communications of the ACM
Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system

CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Recommendation as classification: using social and content-based information in recommendation

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Combining collaborative filtering with personal agents for better recommendations

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Content-based book recommending using learning for text categorization

DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms

Proceedings of the 10th international conference on World Wide Web
Learning Collaborative Information Filters

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Content-boosted collaborative filtering for improved recommendations

Eighteenth national conference on Artificial intelligence
Unifying collaborative and content-based filtering

ICML '04 Proceedings of the twenty-first 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

Cooperative Techniques Supporting Sensor-Based People-Centric Inferencing

Pervasive '08 Proceedings of the 6th International Conference on Pervasive Computing
Machine learning and agents

KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications

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Abstract

In information retrieval, feedback provided by individual users is often very sparse. Consequently, machine learning algorithms for automatically retrieving documents or recommending items may not achieve satisfactory levels of accuracy. However, if one views users as members of a larger user community, then it should be possible to leverage similarities between different users to overcome the sparseness problem. The paper proposes a collaborative machine learning framework to exploit inter-user similarities. More specifically, we present a kernel-based learning architecture that generalizes the well-known Support Vector Machine learning approach by enriching content descriptors with inter-user correlations.