Truthful auctions for pricing search keywords
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Optimal delivery of sponsored search advertisements subject to budget constraints
Proceedings of the 8th ACM conference on Electronic commerce
Allocating online advertisement space with unreliable estimates
Proceedings of the 8th ACM conference on Electronic commerce
AdWords and generalized online matching
Journal of the ACM (JACM)
Predictive user click models based on click-through history
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
An empirical analysis of sponsored search performance in search engine advertising
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Predicting product duration for adaptive advertisement
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
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Search engine advertising has become the main stream of online advertising system. In current search engine advertising systems, all users will get the same advertisement rank if they use the same query. However, different users may have different degree of interest to each advertisement even though they query the same word. In other words, users prefer to click the interested ad by themselves. For this reason, it is important to be able to accurately estimate the interests of individual users and schedule the advertisements with respect to individual users' favorites. For users that have rich history queries, their interests can be evaluated using their query logs. For new users, interests are calculated by summarizing the interests of other users who use similar queries. In this paper, we provide a model to automatically learn individual user's interests based on features of user history queries, user history views of advertisements, user history clicks of advertisements. Then, advertisement schedule is performed according to individual user's interests in order to raise the clickthrough rate of search engine advertisements in response to each user's query. We simulate user's interests of ads and clicks in our experiments. As a result, our personalized ranking scheme of delivering online ads can increase both search engine revenues and users' satisfactions.