research-article

A semantic approach to recommending text advertisements for images

Authors:

Yong YuAuthors Info & Claims

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

Pages 179 - 186

https://doi.org/10.1145/2365952.2365987

Published: 09 September 2012 Publication History

Abstract

In recent years, more and more images have been uploaded and published on the Web. Along with text Web pages, images have been becoming important media to place relevant advertisements. Visual contextual advertising, a young research area, refers to finding relevant text advertisements for a target image without any textual information (e.g., tags). There are two existing approaches, advertisement search based on image annotation, and more recently, advertisement matching based on feature translation between images and texts. However, the state of the art fails to achieve satisfactory results due to the fact that recommended advertisements are syntactically matched but semantically mismatched. In this paper, we propose a semantic approach to improving the performance of visual contextual advertising. More specifically, we exploit a large high-quality image knowledge base (ImageNet) and a widely-used text knowledge base (Wikipedia) to build a bridge between target images and advertisements. The image-advertisement match is built by mapping images and advertisements into the respective knowledge bases and then finding semantic matches between the two knowledge bases. The experimental results show that semantic match outperforms syntactic match significantly using test images from Flickr. We also show that our approach gives a large improvement of 16.4% on the precision of the top 10 matches over previous work, with more semantically relevant advertisements recommended.

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Cited By

Chen JZheng HJiang YXia SZhao C(2019)A probabilistic model for semantic advertisingKnowledge and Information Systems10.1007/s10115-018-1160-759:2(387-412)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s10115-018-1160-7
Filho RWehrmann JBarros R(2017)Leveraging deep visual features for content-based movie recommender systems2017 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2017.7965908(604-611)Online publication date: May-2017
https://doi.org/10.1109/IJCNN.2017.7965908
Rahutomo FAritsugi M(2014)Econo-ESA in semantic text similaritySpringerPlus10.1186/2193-1801-3-1493:1Online publication date: 19-Mar-2014
https://doi.org/10.1186/2193-1801-3-149

Index Terms

A semantic approach to recommending text advertisements for images
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Conferences

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

September 2012

376 pages

ISBN:9781450312707

DOI:10.1145/2365952

General Chairs:
Pádraig Cunningham
University College Dublin, Ireland
,
Neil Hurley
University College Dublin, Ireland
,
Program Chairs:
Ido Guy
IBM Haifa Research Laboratory, Israel
,
Sarabjot Singh Anand
University of Warwick, England

Copyright © 2012 ACM.

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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 09 September 2012

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RecSys '12

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SIGCHI

RecSys '12: Sixth ACM Conference on Recommender Systems

September 9 - 13, 2012

Dublin, Ireland

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RecSys '12 Paper Acceptance Rate 24 of 119 submissions, 20%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Chen JZheng HJiang YXia SZhao C(2019)A probabilistic model for semantic advertisingKnowledge and Information Systems10.1007/s10115-018-1160-759:2(387-412)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s10115-018-1160-7
Filho RWehrmann JBarros R(2017)Leveraging deep visual features for content-based movie recommender systems2017 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2017.7965908(604-611)Online publication date: May-2017
https://doi.org/10.1109/IJCNN.2017.7965908
Rahutomo FAritsugi M(2014)Econo-ESA in semantic text similaritySpringerPlus10.1186/2193-1801-3-1493:1Online publication date: 19-Mar-2014
https://doi.org/10.1186/2193-1801-3-149

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