A semantic approach to contextual advertising

By Andrei Z. Broder, Marcus Felipe M. C. Da Fontoura, Vanja Josifovski, Lance Riedel. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007

356 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/5dfdd109be166ae6c33eb2749258225c06d0ddd3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiang Yang
2. Olivier Chapelle
3. Hang Li
4. Jun Xu
5. Yi Chang
6. Weinan Zhang
7. Jun Wang
8. Jianfeng Gao

In Information Systems

1. Olivier Chapelle
2. Hang Li
3. Jun Xu
4. Shuai Yuan
5. Yi Chang
6. Jun Wang
7. Weinan Zhang
8. Qiang Yang

Citations in the corpus

• Audience selection for on-line brand advertising: privacy-friendly social network targeting (2009, background): “Unlike this prior work and most actual on-line advertising [4, 3] (especially when measured by current advertising spending), the focus of our research is on finding and evaluating audiences for brand advertising.”
• Visual Contextual Advertising: Bringing Textual Advertisements to Images (2010, background): “Researchers on contextual advertising area have proposed various ways to deal with the problem, e.g. (Ribeiro-Neto et al. 2005; Lacerda et al. 2006; Broder et al. 2007).”
• Promoting Positive Post-Click Experience for In-Stream Yahoo Gemini Users (2015, background): “• The bounce rate is the percentage of ad clicks whose dwell time, unless otherwise specified, is lower than or equal to a fixed threshold.”
• Improving Post-Click User Engagement on Native Ads via Survival Analysis (2016, background): “To optimize for CTR, most efforts have been around improving the matching between web queries (in sponsored search) or web pages (in display advertising) and the ad textual content (creative and/or bid phrases and title) [5, 4, 7, 22, 25].”
• Yahoo! Learning to Rank Challenge Overview (2010, background): “In the context of contextual advertising, details can be found in (Broder et al., 2007).”
• Optimal real-time bidding for display advertising (2014, methodology): “The DSP uses both contextual [6] (e.g. domain, webpage, keywords, time and date, geographical location, weather, language, operating system, browser, etc.) and behavioural [22] (e.g. search, browsing, and purchase history, occupation, income, sentiment, etc.) data to compute a bid.”
• Real-time bidding for online advertising: measurement and analysis (2013, neutral): “The understanding of user is usually referred to as behaviour targeting, where ad networks utilise the browsing history of a specific user to infer his interests, as well as geographical location, local time, etc for target matching [32, 23, 30].”
• Just-in-time contextual advertising (2007, methodology): “In this section we give a brief overview of the current practices in Web advertising, which is based on a longer presentation in our earlier work [5].”
• Recommender Systems (2016, neutral): “Content-based methods are particularly popular [105, 142, 327], and they use content similarity to match the context of the Web page with that in the advertisements.”
• How much can behavioral targeting help online advertising? (2009, neutral): “With the rapid growth of the World Wide Web (WWW), online advertising channels, such as sponsored search [4], contextual ads [1], and Behavioral Targeting (BT), are showing great market potentials.”
• Semantic Matching in Search (2014, background): “Methods have been proposed for addressing the mismatch challenge at form aspect, sense aspect, and topic aspect [31].”