TY - GEN
T1 - Learning to display in sponsored search
AU - Xin, Xin
AU - Huang, Heyan
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - In sponsored search, it is necessary for the search engine, to decide the right number of advertisements (ads) to display for each query, in the constraint of a limited commercial load. Because over displaying ads will lead to the commercial overload problem, driving some of the users away in the long run. Despite the importance of the issue, very few literatures have discussed about how to measure the commercial load in sponsored search. Thus it is difficult for the search engine to make decisions quantitatively in practice. As a primary study, we propose to quantify the commercial load by the average displayed ad number per query, and then we investigate the displaying strategy to optimize the total revenue, in the constraint of a limited commercial load. We formalize this task under the framework of the secretary problem. A novel dynamic algorithm is proposed, which is extended from the state-of-theart multiple-choice secretary algorithm. Through theoretical analysis, we proof that our algorithm is approaching the optimal value; and through empirical analysis, we demonstrate that our algorithm outperforms the fundamental static algorithm significantly. The algorithm can scale up with respect to very large datasets.
AB - In sponsored search, it is necessary for the search engine, to decide the right number of advertisements (ads) to display for each query, in the constraint of a limited commercial load. Because over displaying ads will lead to the commercial overload problem, driving some of the users away in the long run. Despite the importance of the issue, very few literatures have discussed about how to measure the commercial load in sponsored search. Thus it is difficult for the search engine to make decisions quantitatively in practice. As a primary study, we propose to quantify the commercial load by the average displayed ad number per query, and then we investigate the displaying strategy to optimize the total revenue, in the constraint of a limited commercial load. We formalize this task under the framework of the secretary problem. A novel dynamic algorithm is proposed, which is extended from the state-of-theart multiple-choice secretary algorithm. Through theoretical analysis, we proof that our algorithm is approaching the optimal value; and through empirical analysis, we demonstrate that our algorithm outperforms the fundamental static algorithm significantly. The algorithm can scale up with respect to very large datasets.
UR - http://www.scopus.com/inward/record.url?scp=84915756304&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-13186-3_33
DO - 10.1007/978-3-319-13186-3_33
M3 - Conference contribution
AN - SCOPUS:84915756304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 357
EP - 368
BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops
A2 - Peng, Wen-Chih
A2 - Wang, Haixun
A2 - Zhou, Zhi-Hua
A2 - Ho, Tu Bao
A2 - Tseng, Vincent S.
A2 - Chen, Arbee L.P.
A2 - Bailey, James
PB - Springer Verlag
T2 - International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014
Y2 - 13 May 2014 through 16 May 2014
ER -
