Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Machine Learning
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Query type classification for web document retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Hidden Conditional Random Fields for Gesture Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Query enrichment for web-query classification
ACM Transactions on Information Systems (TOIS)
Robust classification of rare queries using web knowledge
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th international conference on World wide web
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Web Query Recommendation via Sequential Query Prediction
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Capturing user intent for information retrieval
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Context-Aware Online Commercial Intention Detection
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Precomputing search features for fast and accurate query classification
Proceedings of the third ACM international conference on Web search and data mining
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Actively predicting diverse search intent from user browsing behaviors
Proceedings of the 19th international conference on World wide web
A user-item relevance model for log-based collaborative filtering
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Proceedings of the sixth ACM international conference on Web search and data mining
Measuring personalization of web search
Proceedings of the 22nd international conference on World Wide Web
On cognition, emotion, and interaction aspects of search tasks with different search intentions
Proceedings of the 22nd international conference on World Wide Web
"Piaf" vs "Adele": classifying encyclopedic queries using automatically labeled training data
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Fast topic discovery from web search streams
Proceedings of the 23rd international conference on World wide web
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Understanding user intent from her sequential search behaviors, i.e. predicting the intent of each user query in a search session, is crucial for modern Web search engines. However, due to the huge number of user behavior variables and coarse level intent labels defined by human editors, it is very difficult to directly model user behavioral dynamics or user intent dynamics in user search sessions. In this paper, we propose a novel Sparse Hidden-Dynamic Conditional Random Fields (SHDCRF) model for user intent learning from their search sessions. Through incorporating the proposed hidden state variables, SHDCRF aims to learn a substructure, i.e. a set of related hidden variables, for each intent label and they are used to model the intermediate dynamics between user intent labels and user behavioral variables. In addition, SHDCRF learns a sparse relation between the hidden variables and intent labels to make the hidden state variables explainable. Extensive experiment results, on real user search sessions from a popular commercial search engine show that the proposed SHDCRF model significantly outperforms in terms of intent prediction results that those classical solutions such as Support Vector Machine (SVM), Conditional Random Field (CRF) and Latnet-Dynamic Conditional Random Field (LDCRF).