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A semantic transfer approach to keyword suggestion for search engine advertising

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Abstract

Search Engine Advertising has been widely adopted by advertisers to target potential consumers. However, the advertisers generally focus on limited popular advertising keywords, leading to fierce competition. Therefore, abundant relevant keywords need to be discovered to reduce the advertising cost. In this regard, this paper proposes a novel semantic transfer approach (named STAKS) to suggesting keyword for search engine advertising. Compared with the existing methods which explore keywords with direct relevance to the given seed keyword, STAKS can find keywords with multi-step indirect relevance through semantic paths. Moreover, three pruning strategies are designed to (1) ensure the relevance between the suggested keywords and the seed keywords, (2) narrow the semantic drift and (3) reduce the computational consumption. Data experiments show the superiority of STAKS which finds more novel keywords, owing to the indirect relevance ignored by existing methods. Therefore, STAKS is deemed effective in supporting the advertisers to achieve high advertising impressions with relatively low bidding prices.

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Data Availability Statement

The data used in the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (71772177/72072177), and the MOE Project of Key Research Institute of Humanities and Social Sciences at Universities (17JJD630006). This research is also supported by Public Computing Cloud, Renmin University of China.

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Authors and Affiliations

  1. Department of Management Science and Engineering, School of Business, Renmin University of China, Beijing, 100872, China

    Jin Zhang & Jilong Zhang

  2. China Retail Research Center, School of Economics and Management, Tsinghua University, Beijing, 100084, China

    Guoqing Chen

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  1. Jin Zhang
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Correspondence to Jilong Zhang.

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Zhang, J., Zhang, J. & Chen, G. A semantic transfer approach to keyword suggestion for search engine advertising. Electron Commer Res 23, 921–947 (2023). https://doi.org/10.1007/s10660-021-09496-7

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