NDLI: Data-driven text features for sponsored search click prediction

Please wait, while we are loading the content...

Brand advertising, on-line audiences, and social media: invited talk

A Markov chain model for integrating behavioral targeting into contextual advertising

Probabilistic latent semantic user segmentation for behavioral targeted advertising

Argo: intelligent advertising by mining a user's interest from his photo collections

Scalable clustering and keyword suggestion for online advertisements

Inferring local synonyms for improving keyword suggestion in an on-line advertisement system

Data-driven text features for sponsored search click prediction

Revenue optimization with relevance constraint in sponsored search

Pricing guidance in ad sale negotiations: the PrintAds example

Online allocation of display advertisements subject to advanced sales contracts

Handling missing values in GPS surveys using survival analysis: a GPS case study of outdoor advertising

Ad quality on TV: predicting television audience retention

Data-driven text features for sponsored search click prediction

Content Provider	ACM Digital Library
Author	Shaparenko, Benyah Çetin, Özgür Iyer, Rukmini
Abstract	Much search engine revenue comes from sponsored search ads displayed with algorithmic search results. To maximize revenue, it is essential to choose a good slate of ads for each query, requiring accurate prediction of whether or not users will click on an ad. Click prediction is relatively easy for the ads that have been displayed many times, and have significant click history, but in the long tail with minimal or no click history, other features are needed to predict user response. In this work, we investigate the use of novel text features for this problem, within the context of a state-of-the-art sponsored search system. In particular, we propose the use of detailed word-pair indicator features between the query and ad. We compare the new features to the traditional vector-space and language modeling features extracted in a typical information-retrieval style. We evaluate these approaches in a maximum-entropy ranking model using the click-view data from a commercial search-engine traffic. We show that the word-pair features are highly helpful for sponsored search click prediction, not only improving over the sophisticated click-history feedback based systems, but also compensating for the lack of click history to some extent. In contrast, we find that the language and vector-space modeling approaches are significantly less effective.
Starting Page	46
Ending Page	54
Page Count	9
File Format	PDF
ISBN	9781605586717
DOI	10.1145/1592748.1592755
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Language model Vector-space model Click prediction Text features Maximum entropy model Sponsored search
Content Type	Text
Resource Type	Article

Central Library (ISO-9001:2015 Certified)
Indian Institute of Technology Kharagpur
Kharagpur, West Bengal, India | PIN - 721302

See location in the Map
03222 282435
Mail: support@ndl.gov.in

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in