Sponsored ad-based similarity: an approach to mining collective advertiser intelligence

Authors:
Jessica Staddon
Affiliations:
Palo Alto Research Center
Venue:
Proceedings of the 2nd International Workshop on Data Mining and Audience Intelligence for Advertising
Year:
2008

Citing 8
Cited 0

Using collaborative filtering to weave an information tapestry

Communications of the ACM - Special issue on information filtering
Recommender systems

Communications of the ACM
An algorithmic framework for performing collaborative filtering

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering

IEEE Internet Computing
SimRank: a measure of structural-context similarity

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling link-based similarity search

WWW '05 Proceedings of the 14th international conference on World Wide Web
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)

Quantified Score

Hi-index	0.01

Visualization

Abstract

We present a method for mining the intelligence of advertisers to detect product similarities and generate accurate recommendations. In contrast to conventional recommendation algorithms, our approach is completely automated and relies solely on publicly available data, namely, the linking of advertisements with Web content. We present a general framework for leveraging linked advertisements to detect object similarity, and provide experimental evidence that the approach yields useful product recommendations.