Convex Optimization
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Implicit user modeling for personalized search
Proceedings of the 14th ACM international conference on Information and knowledge management
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying "best bet" web search results by mining past user behavior
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A field study characterizing Web-based information-seeking tasks
Journal of the American Society for Information Science and Technology
Investigating behavioral variability in web search
Proceedings of the 16th international conference on World Wide Web
Predictive user click models based on click-through history
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Web search strategies: The influence of Web experience and task type
Information Processing and Management: an International Journal
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
Characterizing the influence of domain expertise on web search behavior
Proceedings of the Second ACM International Conference on Web Search and Data Mining
How much can behavioral targeting help online advertising?
Proceedings of the 18th international conference on World wide web
Modeling and predicting user behavior in sponsored search
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting bounce rates in sponsored search advertisements
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Context-aware prediction of user's first click
Proceedings of the 1st International Workshop on Context Discovery and Data Mining
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Predicting user's action provides many monetization opportunities to web service providers. If a user's future action can be predicted and identified correctly in time or in advance, we cannot only satisfy user's current need, but also facilitate and simplify user's future online activities. Traditional works on user behavior modeling such as implicit feedback or personalization mainly investigate on users' immediate, short-term or aggregate behaviors. Hence, it is difficult to understand the diversity in temporal user behavior and predict user's future action. In this paper, we consider a forecasting problem of temporal user behavior modeling. Our first objective is able to capture relevant users that will perform an action. The second objective is able to identify whether a user has finished the action, even when the action happened offline. We propose an ensemble algorithm to achieve both objectives. The experiment compares several implementation methods and demonstrates the temporal user behavior modeling using the ensemble algorithm significantly outperforms other methods.