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SoRTESum: A Social Context Framework for Single-Document Summarization Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Implicit Look-Alike Modelling in Display Ads

Transfer Collaborative Filtering to CTR Estimation

Authors : Weinan Zhang, Lingxi Chen, Jun Wang

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

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Abstract

User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users’ interest profiles via tracking their online behaviour and then delivers the relevant ads according to each user’s interest, which leads to higher targeting accuracy and thus more improved advertising performance. The current user profiling methods include building keywords and topic tags or mapping users onto a hierarchical taxonomy. However, to our knowledge, there is no previous work that explicitly investigates the user online visits similarity and incorporates such similarity into their ad response prediction. In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response. Technically, we propose a transfer learning model based on the probabilistic latent factor graphic models, where the users’ ad response profiles are generated from their online browsing profiles. The large-scale experiments based on real-world data demonstrate significant improvement of our solution over some strong baselines.

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Footnotes
1
All the features studied in our work are one-hot encoded binary features.
 
2
It is common to perform negative down sampling to balance the labels in ad CTR estimation [9]. Calibration methods [3] are then leveraged to eliminate the model bias.
 
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Metadata
Title
Implicit Look-Alike Modelling in Display Ads
Authors
Weinan Zhang
Lingxi Chen
Jun Wang
Copyright Year
2016
Publisher
Springer International Publishing
Book
Advances in Information Retrieval

Print ISBN: 978-3-319-30670-4

Electronic ISBN: 978-3-319-30671-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-30671-1_43