The Experts below are selected from a list of 102 Experts worldwide ranked by ideXlab platform

Carlo Zaniolo - One of the best experts on this subject based on the ideXlab platform.

  • ICDM - Max-Intensity: Detecting Competitive Advertiser Communities in Sponsored Search Market
    2015 IEEE International Conference on Data Mining, 2015
    Co-Authors: Wenchao Yu, Justin Wood, Wei Wang, Carlo Zaniolo
    Abstract:

    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.

  • Max-Intensity: Detecting Competitive Advertiser Communities in Sponsored Search Market
    2015 IEEE International Conference on Data Mining, 2015
    Co-Authors: Wenchao Yu, Justin Wood, Wei Wang, Carlo Zaniolo
    Abstract:

    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 - One of the best experts on this subject based on the ideXlab platform.

  • ICDM - Max-Intensity: Detecting Competitive Advertiser Communities in Sponsored Search Market
    2015 IEEE International Conference on Data Mining, 2015
    Co-Authors: Wenchao Yu, Justin Wood, Wei Wang, Carlo Zaniolo
    Abstract:

    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.

  • Max-Intensity: Detecting Competitive Advertiser Communities in Sponsored Search Market
    2015 IEEE International Conference on Data Mining, 2015
    Co-Authors: Wenchao Yu, Justin Wood, Wei Wang, Carlo Zaniolo
    Abstract:

    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.

Justin Wood - One of the best experts on this subject based on the ideXlab platform.

  • ICDM - Max-Intensity: Detecting Competitive Advertiser Communities in Sponsored Search Market
    2015 IEEE International Conference on Data Mining, 2015
    Co-Authors: Wenchao Yu, Justin Wood, Wei Wang, Carlo Zaniolo
    Abstract:

    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.

  • Max-Intensity: Detecting Competitive Advertiser Communities in Sponsored Search Market
    2015 IEEE International Conference on Data Mining, 2015
    Co-Authors: Wenchao Yu, Justin Wood, Wei Wang, Carlo Zaniolo
    Abstract:

    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.

Wei Wang - One of the best experts on this subject based on the ideXlab platform.

  • ICDM - Max-Intensity: Detecting Competitive Advertiser Communities in Sponsored Search Market
    2015 IEEE International Conference on Data Mining, 2015
    Co-Authors: Wenchao Yu, Justin Wood, Wei Wang, Carlo Zaniolo
    Abstract:

    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.

  • Max-Intensity: Detecting Competitive Advertiser Communities in Sponsored Search Market
    2015 IEEE International Conference on Data Mining, 2015
    Co-Authors: Wenchao Yu, Justin Wood, Wei Wang, Carlo Zaniolo
    Abstract:

    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.

Djoerd Hiemstra - One of the best experts on this subject based on the ideXlab platform.

  • CIKM - Federated Search in the wild: the combined power of over a hundred Search engines
    Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12, 2012
    Co-Authors: Dong Nguyen, Thomas Demeester, Dolf Trieschnigg, Djoerd Hiemstra
    Abstract:

    Federated Search has the potential of improving web Search: the user becomes less dependent on a single Search Provider and parts of the deep web become available through a unified interface, leading to a wider variety in the retrieved Search results. However, a publicly available dataset for federated Search reflecting an actual web environment has been absent. As a result, it has been difficult to assess whether proposed systems are suitable for the web setting. We introduce a new test collection containing the results from more than a hundred actual Search engines, ranging from large general web Search engines such as Google and Bing to small domain-specific engines. We discuss the design and analyze the effect of several sampling methods. For a set of test queries, we collected relevance judgements for the top 10 results of each Search engine. The dataset is publicly available and is useful for reSearchers interested in resource selection for web Search collections, result merging and size estimation of uncooperative resources.

  • The Combined Power of over a Hundred Search Engines
    2012
    Co-Authors: Dong Nguyen, Thomas Demeester, Dolf Trieschnigg, Djoerd Hiemstra
    Abstract:

    Federated Search has the potential of improving web Search: the user becomes less dependent on a single Search Provider and parts of the deep web become available through a unied interface, leading to a wider variety in the retrieved Search results. However, a publicly available dataset for federated Search reecting an actual web environment has been absent. As a result, it has been dicult to assess whether proposed systems are suitable for the web setting. We introduce a new test collection containing the results from more than a hundred actual Search engines, ranging from large general web Search engines such as Google and Bing to small domain-specic engines. We discuss the design and analyze the eect of several sampling methods. For a set of test queries, we collected relevance judgements for the top 10 results of each Search engine. The dataset is publicly available and is useful for reSearchers interested in resource selection for web Search collections, result merging and size estimation of uncooperative resources.