Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
A large-scale analysis of query logs for assessing personalization opportunities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
To each his own: personalized content selection based on text comprehensibility
Proceedings of the fifth ACM international conference on Web search and data mining
Active objects: actions for entity-centric search
Proceedings of the 21st international conference on World Wide Web
Mining entity types from query logs via user intent modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
RESQ: rank-energy selective query forwarding for distributed search systems
Proceedings of the 21st ACM international conference on Information and knowledge management
Authorship attribution based on a probabilistic topic model
Information Processing and Management: an International Journal
Building user profiles from topic models for personalised search
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Rank-energy selective query forwarding for distributed search systems
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Fast topic discovery from web search streams
Proceedings of the 23rd international conference on World wide web
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We investigate the utility of topic models for the task of personalizing search results based on information present in a large query log. We define generative models that take both the user and the clicked document into account when estimating the probability of query terms. These models can then be used to rank documents by their likelihood given a particular query and user pair.