Yahoo! as an ontology: using Yahoo! categories to describe documents
Proceedings of the eighth international conference on Information and knowledge management
Convex Optimization
Impedance coupling in content-targeted advertising
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Truthful auctions for pricing search keywords
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Probabilistic latent query analysis for combining multiple retrieval sources
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
A semantic approach to contextual advertising
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Estimating rates of rare events at multiple resolutions
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A noisy-channel approach to contextual advertising
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
A combinatorial allocation mechanism with penalties for banner advertising
Proceedings of the 17th international conference on World Wide Web
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
A search-based method for forecasting ad impression in contextual advertising
Proceedings of the 18th international conference on World wide web
Online allocation of display advertisements subject to advanced sales contracts
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
An expressive auction design for online display advertising
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Translating relevance scores to probabilities for contextual advertising
Proceedings of the 18th ACM conference on Information and knowledge management
Forecasting high-dimensional data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Estimating rates of rare events with multiple hierarchies through scalable log-linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Pricing guaranteed contracts in online display advertising
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A stochastic learning-to-rank algorithm and its application to contextual advertising
Proceedings of the 20th international conference on World wide web
Identifying similar people in professional social networks with discriminative probabilistic models
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Bid landscape forecasting in online ad exchange marketplace
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic latent class models for predicting student performance
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Hi-index | 0.00 |
Online display advertising is a multi-billion dollar industry where advertisers promote their products to users by having publishers display their advertisements on popular Web pages. An important problem in online advertising is how to forecast the number of user visits for a Web page during a particular period of time. Prior research addressed the problem by using traditional time-series forecasting techniques on historical data of user visits; (e.g., via a single regression model built for forecasting based on historical data for all Web pages) and did not fully explore the fact that different types of Web pages and different time stamps have different patterns of user visits. In this paper, we propose a series of probabilistic latent class models to automatically learn the underlying user visit patterns among multiple Web pages and multiple time stamps. The last (and the most effective) proposed model identifies latent groups/classes of (i) Web pages and (ii) time stamps with similar user visit patterns, and learns a specialized forecast model for each latent Web page and time stamp class. Compared with a single regression model as well as several other baselines, the proposed latent class model approach has the capability of differentiating the importance of different types of information across different classes of Web pages and time stamps, and therefore has much better modeling flexibility. An extensive set of experiments along with detailed analysis carried out on real-world data from Yahoo! demonstrates the advantage of the proposed latent class models in forecasting online user visits in online display advertising.