In a sponsored search market, the problem of measuring the intensity of competition among advertisers is increasingly gaining prominence today. Usually, search providers want to monitor the advertiser communities that share common bidding keywords, so that they can intervene when competition slackens. However, to the best of our knowledge, not much research has been conducted in identifying advertiser communities and understanding competition within these communities. In this paper we introduce a novel approach to detect competitive communities in a weighted bi-partite network formed by advertisers and their bidding keywords. The proposed approach is based on an advertiser vertex metric called intensity score, which takes the following two factors into consideration: the competitors that bid on the same keywords, and the advertisers' consumption proportion within the community. Evidence shows that when market competition rises, the revenue for a search provider also increases. Our community detection algorithm Max-Intensity is designed to detect communities which have the maximum intensity score. In this paper, we conduct experiments and validate the performance of Max-Intensity on sponsored search advertising data. Compared to baseline methods, the communities detected by our algorithm have low Herfindahl-Hirschman index (HHI) and comprehensive concentration index (CCI), which demonstrates that the communities given by Max-Intensity can capture the structure of the competitive communities.
['Wenchao Yu', 'Ariyam Das', 'Justin Wood', 'Wei Wang', 'Carlo Zaniolo', 'Ping Luo']
Max-Intensity: Detecting Competitive Advertiser Communities in Sponsored Search Market