Computational advertising techniques for targeting relevant ads
Computational Advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the Web. The context depends on the type of advertising and could mean - content where the ad is shown, the user who is viewing the ad or the...
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| Main Authors: | , |
|---|---|
| Format: | Electronic Book |
| Language: | English |
| Published: |
Hanover, Massachusetts
Now Publishers
2014.
|
| Series: | Foundations and trends in information retrieval
volume 8, issue 4-5 |
| Subjects: | |
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| Call Number : | HF 6146 .I58 D283 2014 |
MARC
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| 020 | |a 9781601988324 | ||
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| 040 | |a UPNM |b eng |c UPNM |e rda | ||
| 090 | |a HF 6146 .I58 |b D283 2014 | ||
| 100 | 1 | |a Dave, Kushal |e author | |
| 245 | 1 | 0 | |a Computational advertising |b techniques for targeting relevant ads |c Kushal Dave, Vasudeva Varma |
| 264 | 1 | |a Hanover, Massachusetts |b Now Publishers |c 2014. | |
| 264 | 4 | |a © 2014 | |
| 300 | |a xi, 161 pages |b illustrations |c 24 cm. | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 rdamedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 490 | 1 | |a Foundations and trends in information retrieval |x 1554-0677 |v volume 8, issue 4-5 | |
| 504 | |a Includes bibliographical references | ||
| 505 | 0 | |a 1. Introduction --1.1 Introduction to computational advertising --1.2 Issues and challenges --1.3 Scope of the survey --1.4 Organization of the survey 2. Finding advertising keywords on web pages --2.1 Keyword extraction as a classification task --2.2 Pattern based keyword extraction --2.3 Using external resources --2.4 Multi-label learning with millions of labels --2.5 Summary 3. Dealing with short text in ads for contextual advertising --3.1 Expanding vocabulary to overcome vocabulary mismatch --3.2 Leveraging taxonomy --3.3 Combining semantics with the syntax --3.4 Topic modeling --3.5 Matching concepts --3.6 Machine learning approach to ad retrieval --3.7 Time-constrained retrieval of ads for web pages --3.8 Dealing with the sentiments in the content --3.9 Summary 4. Handling the short search query for sponsored search --4.1 Query substitution and rewriting --4.2 Leveraging ad-click data for ad retrieval --4.3 Summary 5. Ad quality and spam --5.1 Determining ad quality based on relevance --5.2 Exploiting structural features to find adversarial ads --5.3 Identify when to (not) show ads --5.4 Predicting bounce rate of an ad --5.5 Identifying click spam --5.6 Summary 6. Ranking retrieved ads for sponsored search --6.1 Modeling presentation and position bias --6.2 Predicting the click-through rates of ads --6.3 Ranking ads by machine learning ranking (MLR) --6.4 Impression forecasting --6.5 Summary 7. Ranking ads in contextual advertising --7.1 Learning to rank techniques for ranking ads --7.2 Using hierarchies to impute CTR --7.3 Combining collaborative filtering with feature based models --7.4 Click prediction in display advertising --7.5 Ads ranking --going ahead 8. How much can behavioral targeting help online advertising? --8.1 Analyzing user behavior --8.2 Profile based user targeting --8.3 Personalized click prediction --8.4 Moving over to display advertising 9. Display advertising and real time bidding --9.1 RTB ecosystem --9.2 How real time bidding happens? --9.3 Benefits of RTB --9.4 Contrasting display advertising and contextual advertising --9.5 Summary 10. Emerging topics in computational advertising --10.1 Blurred line between DA, ConAd, SS --10.2 Advertising in a stream/newsfeed --10.3 Social targeting --10.4 Advertising on handheld devices --10.5 Interactive and incentive based advertising 11. Resources --11.1 Datasets --11.2 Relevant conferences and journals --11.3 Academic courses in computational advertising 12. Summary and concluding remarks --12.1 Is ad retrieval/ranking a solved problem? -- 12.2 Research topics --12.3 Conclusion | |
| 520 | |a Computational Advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the Web. The context depends on the type of advertising and could mean - content where the ad is shown, the user who is viewing the ad or the social network of the user. Computational Advertising (CA) is a scientific sub-discipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search and text analysis. The core problem addressed in Computational Advertising is of match-making between the ads and the context | ||
| 592 | |a JI 4860 |b 05/01/2016 |c RM 465.30 |h Jendela Informasi | ||
| 650 | 0 | |a Internet advertising | |
| 700 | 1 | |a Varma, Vasudeva |e author | |
| 700 | 1 | |a Dave, Kushal |e author | |
| 830 | 0 | |a Foundations and trends in information retrieval |v volume 8, issue 4-5 | |
| 999 | |a vtls000055727 |c 97782 |d 97782 | ||