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Optimizing relevance and revenue in ad search: a query substitution approach

Published: 20 July 2008 Publication History

Abstract

The primary business model behind Web search is based on textual advertising, where contextually relevant ads are displayed alongside search results. We address the problem of selecting these ads so that they are both relevant to the queries and profitable to the search engine, showing that optimizing ad relevance and revenue is not equivalent. Selecting the best ads that satisfy these constraints also naturally incurs high computational costs, and time constraints can lead to reduced relevance and profitability. We propose a novel two-stage approach, which conducts most of the analysis ahead of time. An offine preprocessing phase leverages additional knowledge that is impractical to use in real time, and rewrites frequent queries in a way that subsequently facilitates fast and accurate online matching. Empirical evaluation shows that our method optimized for relevance matches a state-of-the-art method while improving expected revenue. When optimizing for revenue, we see even more substantial improvements in expected revenue.

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    cover image ACM Conferences
    SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
    July 2008
    934 pages
    ISBN:9781605581644
    DOI:10.1145/1390334
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 20 July 2008

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    Author Tags

    1. online advertising
    2. relevance
    3. revenue

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    • (2019)A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendationProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3346998(20-28)Online publication date: 10-Sep-2019
    • (2019) Swat : A system for detecting salient Wikipedia entities in texts Computational Intelligence10.1111/coin.1221635:4(858-890)Online publication date: 6-May-2019
    • (2019)Learning Theory and Algorithms for Revenue Maximization in Sponsored Search2019 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2019.00069(434-440)Online publication date: Nov-2019
    • (2018)Scalable Query N-Gram Embedding for Improving Matching and Relevance in Sponsored SearchProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3219897(52-61)Online publication date: 19-Jul-2018
    • (2018)Turning Clicks into PurchasesThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3209993(365-374)Online publication date: 27-Jun-2018
    • (2017)Investigating the Spillover Effect of Keyword Market Entry in Sponsored Search AdvertisingMarketing Science10.1287/mksc.2017.105336:6(976-998)Online publication date: 1-Nov-2017
    • (2017)Optimizing Trade-offs Among Stakeholders in Real-Time Bidding by Incorporating Multimedia MetricsProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080802(205-214)Online publication date: 7-Aug-2017
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    • (2016)Online Evaluation for Information RetrievalFoundations and Trends in Information Retrieval10.1561/150000005110:1(1-117)Online publication date: 1-Jun-2016
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