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Forecasting Ad-Impressions on Online Retail Websites using Non-homogeneous Hawkes Processes

Published: 06 November 2017 Publication History

Abstract

Promotional listing of products or advertisements is a major source of revenue for online retail companies. These advertisements are often sold in the guaranteed delivery market, serving of which critically depends on the ability to predict supply or potential impressions from a target segment of users. In this paper, we study the problem of predicting user visits or potential ad-impressions to online retail websites, based on historical time-stamps. We explore the time-series and temporal point process models. We find that a successful model must encompass three properties of the data: (1) temporally non-homgeneous rates, (2) self excitation and (3) handling special events. We propose a novel non-homogeneous Hawkes process based model for the same, and new algorithm for fitting this model without overfitting the self-excitation part. We validate the proposed model and algorithm using mulitple large scale ad-serving dataset from a top online retail company in India.

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Cited By

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  • (2022)Sequential Recommendation Based on Multivariate Hawkes Process Embedding With AttentionIEEE Transactions on Cybernetics10.1109/TCYB.2021.307736152:11(11893-11905)Online publication date: Nov-2022
  • (2021)Efficient Ad-level Impression Forecasting based on Monotonicity and Sampling2021 7th International Conference on Big Data Computing and Communications (BigCom)10.1109/BigCom53800.2021.00012(180-187)Online publication date: Aug-2021
  • (2018)Demarcating Endogenous and Exogenous Opinion Diffusion Process on Social NetworksProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186121(549-558)Online publication date: 10-Apr-2018

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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
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 the author(s) 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: 06 November 2017

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Author Tags

  1. hawkes processes
  2. non-stationary time-series forecasting
  3. online advertising
  4. supply forecasting

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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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Cited By

View all
  • (2022)Sequential Recommendation Based on Multivariate Hawkes Process Embedding With AttentionIEEE Transactions on Cybernetics10.1109/TCYB.2021.307736152:11(11893-11905)Online publication date: Nov-2022
  • (2021)Efficient Ad-level Impression Forecasting based on Monotonicity and Sampling2021 7th International Conference on Big Data Computing and Communications (BigCom)10.1109/BigCom53800.2021.00012(180-187)Online publication date: Aug-2021
  • (2018)Demarcating Endogenous and Exogenous Opinion Diffusion Process on Social NetworksProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186121(549-558)Online publication date: 10-Apr-2018

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