Bid Keyword Suggestion in Sponsored Search based on Competitiveness and Relevance

Ying Zhang, Weinan Zhang, Bin Gao, Xiaojie Yuan, and Tie-Yan Liu

Abstract

In sponsored search, many advertisers have not achieved their expected performances while the search engine also has a large room to improve their revenue. Specifically, due to the improper keyword bidding, many advertisers cannot survive the competitive ad auctions to get their desired ad impressions; meanwhile, a significant portion of search queries have no ads displayed in their search result pages, even if many of them have commercial values. We propose recommending a group of relevant yet less-competitive keywords to an advertiser. Hence, the advertiser can get the chance to win some (originally empty) ad slots and accumulate a number of impressions. At the same time, the revenue of the search engine can also be boosted since many empty ad shots are filled. Mathematically, we model the problem as a mixed integer programming problem, which maximizes the advertiser revenue and the relevance of the recommended keywords, while minimizing the keyword competitiveness, subject to the bid and budget constraints. By solving the problem, we can offer an optimal group of keywords and their optimal bid prices to an advertiser. Simulation results have shown the proposed method is highly effective in increasing ad impressions, expected clicks, advertiser revenue, and search engine revenue.

Details

Publication typeArticle
Published inInformation Processing and Management
PublisherElsevier
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