Advertising keyword suggestion based on concept hierarchy

The increasing growth of the World Wide Web constantly enlarges the revenue generated by search engine advertising. Advertisers bid on keywords associated with their products to display their ads on the search result pages. Keyword suggestion methods are proposed to fill the gap between the keywords chosen by advertisers and the popular queries, through finding new relevant keywords according to some statistical information (for example, the keyword co-occurrence). However, there is little effort taking semantic information, such as concept hierarchy, into account. In this paper, we propose a novel keyword suggestion method that fully exploits the semantic knowledge among concept hierarchy. Given a keyword, we first match it with some relevant concepts. Then the relevant concepts are used with their hierarchy to fertilize the meanings of the keywords. Finally new keywords are suggested according to the concept information rather than the statistical co-occurrence of the keyword itself. Experimental results show that our proposed method can successfully provide suggestion that meets the accuracy and coverage requirements

[1]  James Allan,et al.  Automatic Query Expansion Using SMART: TREC 3 , 1994, TREC.

[2]  D. K. Harmon,et al.  Overview of the Third Text Retrieval Conference (TREC-3) , 1996 .

[3]  Vibhanshu Abhishek,et al.  Keyword generation for search engine advertising using semantic similarity between terms , 2007, ICEC.

[4]  Adolfo Guzmán-Arenas,et al.  Document Indexing with a Concept Hierarchy , 2005, Computación y Sistemas.

[5]  Rajeev Motwani,et al.  Keyword Generation for Search Engine Advertising , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[6]  Steffen Staab,et al.  Learning taxonomic relations from heterogeneous sources , 2004 .

[7]  Daphne Koller,et al.  Hierarchically Classifying Documents Using Very Few Words , 1997, ICML.

[8]  Hussein A. Abbass,et al.  Learning text classifier using the domain concept hierarchy , 2002, IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions.

[9]  Jennifer Widom,et al.  Exploiting hierarchical domain structure to compute similarity , 2003, TOIS.

[10]  Berthier A. Ribeiro-Neto,et al.  Impedance coupling in content-targeted advertising , 2005, SIGIR '05.

[11]  Vijay Murthi,et al.  Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation , 2006 .

[12]  Leonid Zhukov,et al.  Clustering of bipartite advertiser-keyword graph , 2003 .

[13]  Joshua Goodman,et al.  Finding advertising keywords on web pages , 2006, WWW '06.

[14]  Leila Kosseim,et al.  Using Terminology and a Concept Hierarchy for Restricted-Domain Question-Answering , 2006 .

[15]  Carl Gutwin,et al.  KEA: practical automatic keyphrase extraction , 1999, DL '99.

[16]  Robert L. Mercer,et al.  Class-Based n-gram Models of Natural Language , 1992, CL.

[17]  Victoria S. Uren,et al.  Building and applying a concept hierarchy representation of a user profile , 2003, SIGIR '03.

[18]  Víctor Pàmies,et al.  Open Directory Project , 2003 .

[19]  W. Bruce Croft,et al.  Improving the effectiveness of information retrieval with local context analysis , 2000, TOIS.

[20]  Jaana Kekäläinen,et al.  IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.

[21]  Edward A. Fox,et al.  Proceedings of the Fourth ACM conference on Digital Libraries, August 11-14, 1999, Berkeley, CA, USA , 1999 .

[22]  W. Bruce Croft,et al.  Deriving concept hierarchies from text , 1999, SIGIR '99.

[23]  Shui-Lung Chuang,et al.  Liveclassifier: creating hierarchical text classifiers through web corpora , 2004, WWW '04.

[24]  Benjamin Rey,et al.  Generating query substitutions , 2006, WWW '06.

[25]  Clement T. Yu,et al.  Concept hierarchy based text database categorization in a metasearch engine environment , 2000, Proceedings of the First International Conference on Web Information Systems Engineering.

[26]  Ke Wang,et al.  Building Hierarchical Classifiers Using Class Proximity , 1999, VLDB.

[27]  Julio Gonzalo,et al.  Automatic Association of Web Directories with Word Senses , 2003, Computational Linguistics.