Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Cluster-based language models for distributed retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of a very large web search engine query log
ACM SIGIR Forum
Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Proceedings of the 11th international conference on World Wide Web
Communications of the ACM
The Journal of Machine Learning Research
A cross-collection mixture model for comparative text mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Context-sensitive information retrieval using 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
Personalized Search Based on User Search Histories
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
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
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
Mining long-term search history to improve search accuracy
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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 large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
Term feedback for information retrieval with language models
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Discovering and using groups to improve personalized search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Behavior-driven clustering of queries into topics
Proceedings of the 20th ACM international conference on Information and knowledge management
Fast topic discovery from web search streams
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
This work presents a study to bridge topic modeling and personalized search. A probabilistic topic model is used to extract topics from user search history. These topics can be seen as a roughly summary of user preferences and further treated as feedback within the KL-Divergence retrieval model to estimate a more accurate query model. The topics more relevant to current query contribute more in updating the query model which helps to distinguish between relevant and irrelevant parts and filter out noise in user search history. We designed task oriented user study and the results show that: (1) The extracted topics can be used to cluster queries according to topics. (2) The proposed approach improves ranking quality consistently for queries matching user past interests and is robust for queries not matching past interests.