Methods and Metrics for Cold-Start Recommendations

DMCA

Methods and Metrics for Cold-Start Recommendations (2002)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Andrew I. Schein , Alexandrin Popescul , Lyle H. Ungar , David M. Pennock

Venue:	PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL
Citations:	329 - 7 self

BibTeX

@INPROCEEDINGS{Schein02methodsand,
    author = {Andrew I. Schein and Alexandrin Popescul and Lyle H. Ungar and David M. Pennock},
    title = {Methods and Metrics for Cold-Start Recommendations},
    booktitle = {PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL},
    year = {2002},
    pages = {253--260},
    publisher = {}
}

OpenURL

Abstract

We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a nave Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated. We systematically explore three testing methodologies using a publicly available data set, and explain how these methods apply to specific real-world applications. We advocate heuristic recommenders when benchmarking to give competent baseline performance. We introduce a new performance metric, the CROC curve, and demonstrate empirically that the various components of our testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems. Though the emphasis of our testing is on cold-start recommending, our methods for recommending and evaluation are general.

Keyphrases

cold-start recommendation heuristic recommenders competent baseline performance various component cold-start recommending single probabilistic framework croc curve specific real-world application recommender system nave bayes classifier cold-start problem collaborative data strategy combine available data set new performance performance characteristic

DMCA