Contextual advertising relies on matching an advertisement with a page based on its content. Most often advertisers bid on keywords and the ad platform finds the appropriate pages on which these ads can be displayed by matching keywords and phrases with the content. There have been a number of situations where such an approach may fail. A few real world examples are discussed in the paper by Broder et al. [2]:
a page about a famous golfer named “John Maytag” might trigger an ad for “Maytag dishwashers” since Maytag is a popular brand. Another example could be a page describing the Chevy Tahoe truck (a popular vehicle in US) triggering an ad about “Lake Tahoe vacations”. Polysemy is not the only culprit: there is a (maybe apocryphal) story about a lurid news item about a headless body found in a suitcase triggering an ad for Samsonite luggage! In all these examples the mismatch arises from the fact that the ads are not appropriate for the context.
These examples highlight the need for moving from a contextual to conceptual/semantic advertising models. The paper by Broder suggest mapping the pages as well as ads into a common ontology/taxonomy and thereby finding the appropriate higher level concept (Like politics/sports etc) that relates the two. There are very few papers on advertising models since this is such a closely guarded secret and a search companies' substantial revenue is tied to their ad platform's performance.
I believe that advertising is in it's infancy and more interesting approaches would soon replace the current state of the art. One problem is that due to the lack of datasets it becomes quite difficult to be in academia and make a significant contribution towards this area.
The next avenue for advertising seems to be Social advertising. While Facebook has its own approach to social advertising. I think the general idea of Social Advertising is to utilize not just the context of the page but also the social information to better place the advertisement. One question here is whether the ad placement is done to target the user or his/her audience. These might require slightly different models. For example, if my friends are all clikcing on the iphone ad on a social network, the platform might decide to also target ME personally for the marketing the iPhone. On the other hand if it can identify that a lot of users come to visit my profile due to the social media posts I write -- then perhaps the advertising could target them instead.
One potential market for advertising that I think is completely untapped is the referral. Companies are ready to pay huge sums of money to get new clients. Often cell phone companies, stock trading sites and banks launch promotions where they pay upwards of $50 for referring a friend. But when was the last time you actually did that? I think that is the best way to alienate your friends -- by hoarding corporate America's products and services or spamming their inboxes with unwanted referrals. But still, this is a huge market and worth billions -- if we can crack it! One approach I am thinking of is to build a referral platform (perhaps there are some out there -- I just dont know?) -- one which would benefit publishers and advertisers alike. I as a publisher have a (sort of) general sense of what my audience would like. I can for example even decide that I might be willing to share the $50 I receive from the advertiser and pass on the benefit to my readers (since my payoff is in having the readers come to my blog!) -- thus subsidizing that iPhone you wanted to buy. In the current model, there is'nt much incentive for me to share (a few cents???) /pass on the benefit with the final consumer. But for higher valued products, my guess is that It might just as well work right. Moreover, the referral platform manages the entire process thereby making it easier on the advertiser to launch new schemes and manage their inventory of referral programs.
I had intended to write a brief note on some of the recent papers [1-4] on this topic but turned out sharing my thoughts on the advertising instead -- which is perhaps more fun anyway :-).
[1] http://www.cs.cmu.edu/~deepay/
[2] http://portal.acm.org/
[3] www.csulb.edu/web/journals/
[4] http://www2008.org/papers/
I see one of the papers is on keywords extraction. I wonder, if you can recommend some more papers on automatic keywords extraction from texts?
Posted by: Maria Grineva | July 21, 2008 at 03:56 AM
Hi Maria, I have not kept up with literature on keyword extraction very much. However, one of the papers I really like is by Glover et al. "Inferring hierarchical descriptions", Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM'02), November 2002. http://ericglover.com/papers/glover-cikm-2002.ps It presents a very simple, effective technique for identifying descriptive terms for a group of documents within a collection. Once you do this, you can use them to pick descriptive terms (self, parent, child concepts) from the individual documents. A more recent paper that extends some of this work is P. Treeratpituk and J. Callan. (2006.) "Automatically labeling hierarchical clusters." I would be interested in knowing more about the papers you have read on this subject. Thanks for the comments!
Posted by: Akshay Java | July 21, 2008 at 06:52 AM
Thank you for the refs.
I am collecting material for the related works in this area.
One of the basic papers on extracting keywords using tf/idf properties (and organising them into hierarchies) is:
M. Sanderson and W. B. Croft.
Deriving concept hierarchies from text. In Proceedings of the 22nd Annual International ACM SIGIR, 1999.
Posted by: Maria Grineva | July 21, 2008 at 10:21 AM
I saw that you post some PDFs related to this subject on your blog. You may want to consider monetizing those PDFs through the Ads for Adobe PDF service. It is a contextual ad matching program for PDFs similar to Google AdSense.
http://labs.adobe.com/technologies/adsforpdf
Posted by: Cynthia Tillo (Adobe) | July 21, 2008 at 12:48 PM
Hi Cynthia, Thanks for sharing this. This is a great idea! Monetizing PDF documents -- wonderful. However, I have one doubt about this. I dont think I as an author of an academic paper would like it if someone else was to monetize my original content and work that took me several months of work to produce. Do you view this as a problem for ad monetization of PDF documents? I would love to hear your thoughts on this. Thanks!
Posted by: Akshay Java | July 21, 2008 at 12:53 PM
Very interesting piece Akshay. Thanks for it. I recently blogged about the way companies are being approach about social opportunities... and why I think they are confused. It's @ http://net-effect.blogspot.com/
If you have time to check it out... i'd love your thoughts on it.
Posted by: Paul Daigle | July 22, 2008 at 12:23 PM