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A semantic transfer approach to keyword suggestion for search engine advertising

Author

Listed:
  • Jin Zhang

    (Renmin University of China)

  • Jilong Zhang

    (Renmin University of China)

  • Guoqing Chen

    (Tsinghua University)

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.

Suggested Citation

  • Jin Zhang & Jilong Zhang & Guoqing Chen, 2023. "A semantic transfer approach to keyword suggestion for search engine advertising," Electronic Commerce Research, Springer, vol. 23(2), pages 921-947, June.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:2:d:10.1007_s10660-021-09496-7
    DOI: 10.1007/s10660-021-09496-7
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    References listed on IDEAS

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    1. Carsten D. Schultz, 2020. "The impact of ad positioning in search engine advertising: a multifaceted decision problem," Electronic Commerce Research, Springer, vol. 20(4), pages 945-968, December.
    2. Abou Nabout, Nadia & Lilienthal, Markus & Skiera, Bernd, 2014. "Empirical Generalizations in Search Engine Advertising," Journal of Retailing, Elsevier, vol. 90(2), pages 206-216.
    3. Lihua Sun & Junpeng Guo & Yanlin Zhu, 2020. "A multi-aspect user-interest model based on sentiment analysis and uncertainty theory for recommender systems," Electronic Commerce Research, Springer, vol. 20(4), pages 857-882, December.
    4. Zhao Jiang & Wu Dan & Liu Jie, 2020. "Distinct role of targeting precision of Internet-based targeted advertising in duopolistic e-business firms’ heterogeneous consumers market," Electronic Commerce Research, Springer, vol. 20(2), pages 453-474, June.
    5. Arash Asadpour & MohammadHossein Bateni & Kshipra Bhawalkar & Vahab Mirrokni, 2019. "Concise Bid Optimization Strategies with Multiple Budget Constraints," Management Science, INFORMS, vol. 65(12), pages 5785-5812, December.
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