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Volume Ranking and Sequential Selection in Programmatic Display Advertising

Published: 06 November 2017 Publication History

Abstract

Programmatic display advertising, which enables advertisers to make real-time decisions on individual ad display opportunities so as to achieve a precise audience marketing, has become a key technique for online advertising. However, the constrained budget setting still restricts unlimited ad impressions. As a result, a smart strategy for ad impression selection is necessary for the advertisers to maximize positive user responses such as clicks or conversions, under the constraints of both ad volume and campaign budget. In this paper, we borrow in the idea of top-N ranking and filtering techniques from information retrieval and propose an effective ad impression volume ranking method for each ad campaign, followed by a sequential selection strategy considering the remaining ad volume and budget, to smoothly deliver the volume filtering while maximizing campaign efficiency. The extensive experiments on two benchmarking datasets and a commercial ad platform demonstrate large performance superiority of our proposed solution over traditional methods, especially under tight budgets.

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                            cover image ACM Conferences
                            CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
                            November 2017
                            2604 pages
                            ISBN:9781450349185
                            DOI:10.1145/3132847
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                            Published: 06 November 2017

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                            1. article recommendation
                            2. noise contrastive estimation
                            3. text representation
                            4. transfer learning
                            5. word2vec

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                            CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;
                            Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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