Passage-level evidence in document retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Information Processing and Management: an International Journal - Special issue: history of information science
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Stuff I've seen: a system for personal information retrieval and re-use
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Impedance coupling in content-targeted advertising
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Advances in XML Information Retrieval: Third International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2004, Dagstuhl Castle, ... 2004 (Lecture Notes in Computer Science)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Linear feature-based models for information retrieval
Information Retrieval
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A semantic approach to contextual advertising
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Just-in-time contextual advertising
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Optimizing relevance and revenue in ad search: a query substitution approach
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Classifiers without borders: incorporating fielded text from neighboring web pages
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Overview of the INEX 2007 Book Search track: BookSearch '07
ACM SIGIR Forum
Search advertising using web relevance feedback
Proceedings of the 17th ACM conference on Information and knowledge management
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
Online expansion of rare queries for sponsored search
Proceedings of the 18th international conference on World wide web
A Data Structure for Sponsored Search
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Efficient query expansion for advertisement search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A relevance model based filter for improving ad quality
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Utilizing passage-based language models for document retrieval
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Adapting boosting for information retrieval measures
Information Retrieval
Hierarchical language models for XML component retrieval
INEX'04 Proceedings of the Third international conference on Initiative for the Evaluation of XML Retrieval
Predicting web searcher satisfaction with existing community-based answers
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Web Page Summarization for Just-in-Time Contextual Advertising
ACM Transactions on Intelligent Systems and Technology (TIST)
Permutation indexing: fast approximate retrieval from large corpora
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The core task of sponsored search is to retrieve relevant ads for the user's query. Ads can be retrieved either by exact match, when their bid term is identical to the query, or by advanced match, which indexes ads as documents and is similar to standard information retrieval (IR). Recently, there has been a great deal of research into developing advanced match ranking algorithms. However, no previous research has addressed the ad indexing problem. Unlike most traditional search problems, the ad corpus is defined hierarchically in terms of advertiser accounts, campaigns, and ad groups, which further consist of creatives and bid terms. This hierarchical structure makes indexing highly non-trivial, as naively indexing all possible "displayable" ads leads to a prohibitively large and ineffective index. We show that ad retrieval using such an index is not only slow, but its precision is suboptimal as well. We investigate various strategies for compact, hierarchy-aware indexing of sponsored search ads through adaptation of standard IR indexing techniques. We also propose a new ad retrieval method that yields more relevant ads by exploiting the structured nature of the ad corpus. Experiments carried out over a large ad test collection from a commercial search engine show that our proposed methods are highly effective and efficient compared to more standard indexing and retrieval approaches.