A feature-pair-based associative classification approach to look-alike modeling for conversion-oriented user-targeting in tail campaigns

Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011

Volumn , Issue , 2011, Pages 85-86

  Mangalampalli, Ashish ^a   Ratnaparkhi, Adwait ^b   Hatch, Andrew O ^c   Bagherjeiran, Abraham ^c   Parekh, Rajesh ^d   Pudi, Vikram ^a  

^a INTERNATIONAL INSTITUTE OF INFORMATION TECHNOLOGY   (India)

^b 33Across Inc   (United States)

^c YAHOO RESEARCH   (United States)

^d GROUPON INC   (United States)

Author keywords

associative classification; look alike modeling; monetization; user targeting

Indexed keywords

ASSOCIATIVE CLASSIFICATION; ASSOCIATIVE CLASSIFIERS; BEHAVIORAL DATA; CUSTOM MODELS; DATA SETS; LINEAR SVM; LOG LIKELIHOOD RATIO; LOOK-ALIKE MODELING; MONETIZATION; ONLINE ADVERTISING; RULE BASED; TRAIN MODEL; TRAINING DATASET; USER TARGETING;

LUNAR SURFACE ANALYSIS;

WORLD WIDE WEB;

EID: 79955385759     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1963192.1963236     Document Type: Conference Paper

References (2)

1. A. Bagherjeiran, A. O. Hatch, A. Ratnaparkhi, and R. Parekh. Large-scale customized models for advertisers. In ICDM Workshops, pages 1029-1036, 2010.
2. F. A. Thabtah. A review of associative classification mining. Knowledge Eng. Review, 22(1):37-65, 2007.