A semantic approach to recommending text advertisements for images

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.

Econo-ESA in semantic text similarity

“…Researchers have further developed ESA for use in different environments, including information retrieval (Hassan and Mihalcea 2009; Polajnar et al 2013; Potthast et al 2008; Potthast et al 2012; Scholl et al 2010; Sorg and Cimiano 2010; 2012; Tanase and Kapetanios 2012), image retrieval (Popescu and Grefenstette 2011; Zhang et al 2012), semantic text similarity (STS) (Aggarwal et al 2012; Martín et al 2013; Szarvas et al 2011), categorization (O’Banion et al 2012; Szarvas et al 2011; Szczuka et al 2011), machine translation (Matsuno and Ishida 2011), and question-answering (Walter et al 2012). It is also used in knowledge discovery (Yan and Jin 2012), music classification (Aryafar and Shokoufandeh 2011), learning systems (Schmidt et al 2011), text disambiguation (Fernandez et al 2011), and case based reasoning systems (Patelia et al 2011).…”