Clustering-Based Bidding Languages for Sponsored Search

被引:0
|
作者
Mahdian, Mohammad [1 ]
Wang, Grant [1 ]
机构
[1] Yahoo Inc, Santa Clara, CA USA
来源
ALGORITHMS - ESA 2009, PROCEEDINGS | 2009年 / 5757卷
关键词
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Sponsored search auctions provide a marketplace where advertisers can bid for millions of advertising opportunities to promote their products. The main difficulty facing the advertisers in this market is the complexity of picking and evaluating keywords and phrases to bid on. This is due to the sheer number of possible keywords that the advertisers can bid on, and leads to inefficiencies in the market such as lack of coverage for "rare" keywords. Approaches such as broad 'matching have been proposed to alleviate this problem. However, as we will observe in this paper, broad matching has undesirable economic properties (such as the non-existence of equilibria) that can make it hard for an advertiser to determine how much to bid for a broad-matched keyword. The main contribution of this paper is to introduce a bidding language for sponsored search auctions based on broad-matching keywords to non-overlapping clusters that greatly simplifies the bidding problem for the advertisers. We investigate the algorithmic problem of computing the optimal clustering given a set of estimated values and give an approximation algorithm for this problem. Furthermore, we present experimental results using real advertisers' data that show that it is possible to extract close to the optimal social welfare with a number of clusters considerably smaller than the number of keywords. This demonstrates the applicability of the clustering scheme and our algorithm in practice.
引用
收藏
页码:167 / 178
页数:12
相关论文
共 50 条
  • [41] Bidding for Multiple Keywords in Sponsored Search Advertising: Keyword Categories and Match Types
    Du, Xiaomeng
    Su, Meng
    Zhang, Xiaoquan
    Zheng, Xiaona
    INFORMATION SYSTEMS RESEARCH, 2017, 28 (04) : 711 - 722
  • [42] An Evaluation for Different Pricing Mechanisms under the Sponsored Search with Various Bidding Processes
    Tsung, Chen-Kun
    Ho, HannJang
    Lee, SingLing
    2016 INTERNATIONAL COMPUTER SYMPOSIUM (ICS), 2016, : 28 - 32
  • [43] A clustering-based symbiotic organisms search algorithm for high-dimensional optimization problems
    Yang, Chao-Lung
    Sutrisno, Hendri
    APPLIED SOFT COMPUTING, 2020, 97
  • [44] Reducing Interactive Refactoring Effort via Clustering-Based Multi-objective Search
    Alizadeh, Vahid
    Kessentini, Marouane
    PROCEEDINGS OF THE 2018 33RD IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMTED SOFTWARE ENGINEERING (ASE' 18), 2018, : 464 - 474
  • [45] Summarizing Results of Keyword Search on Social Photos using Clustering-based Burst Detection
    Sakai, Tatsuhiro
    Tamura, Keiichi
    2015 IIAI 4TH INTERNATIONAL CONGRESS ON ADVANCED APPLIED INFORMATICS (IIAI-AAI), 2015, : 715 - 716
  • [46] A clustering-based method for fuzzy modeling
    Wong, CC
    Chen, CC
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 1999, E82D (06) : 1058 - 1065
  • [47] Clustering-Based Incremental Web Crawling
    Tan, Qingzhao
    Mitra, Prasenjit
    ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2010, 28 (04)
  • [48] Progressive Exponential Clustering-Based Steganography
    Chang-Tsun Li
    Yue Li
    EURASIP Journal on Advances in Signal Processing, 2010
  • [49] Random clustering-based outlier detector
    Kiersztyn A.
    Pylak D.
    Horodelski M.
    Kiersztyn K.
    Urbanovich P.
    Information Sciences, 2024, 667
  • [50] Conference scheduling: A clustering-based approach
    Bulhoes, Teobaldo
    Correia, Rubens
    Subramanian, Anand
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2022, 297 (01) : 15 - 26