Agrawal, R. ; Gupta, A. ; Prabhu, Y. ; Varma, M. (2013) Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages In: Proceedings of the International World Wide Web Conference, Rio de Janeiro, Brazil.
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Abstract
Recommending phrases from web pages for advertisers to bid on against search engine queries is an important research problem with direct commercial impact. Most approaches have found it infeasible to determine the relevance of all possible queries to a given ad landing page and have focussed on making recommendations from a small set of phrases extracted (and expanded) from the page using NLP and ranking based techniques. In this paper, we eschew this paradigm, and demonstrate that it is possible to efficiently predict the relevant subset of queries from a large set of monetizable ones by posing the problem as a multi-label learning task with each query being represented by a separate label
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Source: | Copyright of this article belongs to International World Wide Web Conference Committee. |
| ID Code: | 119691 |
| Deposited On: | 16 Jun 2021 08:50 |
| Last Modified: | 16 Jun 2021 08:50 |
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