NDLI: Relevance-index size tradeoff in contextual advertising

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Relevance-index size tradeoff in contextual advertising

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Relevance-index size tradeoff in contextual advertising

Content Provider	ACM Digital Library
Author	Leela, Krishna P. Parsana, Mehul Garg, Sachin GM, Pavan Kumar
Abstract	In Contextual advertising, textual ads relevant to the content in a webpage are embedded in the page. Content keywords are extracted offline by crawling webpages and then stored in an index for fast serving. Given a page, ad selection involves index lookup, computing similarity between the keywords of the page and those of candidate ads and returning the top-k scoring ads. In this approach, there is a tradeoff between relevance and index size where better relevance can be achieved if there are no limits on the index size. However, the assumption of unlimited index size is not practical due to the large number of pages on the Web and stringent requirements on the serving latency. Secondly, page visits on the web follows power-law distribution where a significant proportion of the pages are visited infrequently, also called the tail pages. Indexing tail pages is not efficient given that these pages are accessed very infrequently. We propose a novel mechanism to mitigate these problems in the same framework. The basic idea is to index the same keyword vector for a set of similar pages. The scheme involves learning a website specific hierarchy from (page, URL) pairs of the website. Next, keywords are populated on the nodes via bottom-up traversal over the hierarchy. We evaluate our approach on a human labeled dataset where our approach has higher nDCG compared to a recent approach even though the index size of our approach is 7 times less than index size of the recent approach.
Starting Page	1721
Ending Page	1724
Page Count	4
File Format	PDF
ISBN	9781450300995
DOI	10.1145/1871437.1871713
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-10-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Relevance Contextual advertising Keyword aggregation Index size
Content Type	Text
Resource Type	Article

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