A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of the American Society for Information Science and Technology
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A two-stage mixture model for pseudo feedback
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Concept-based interactive query expansion
Proceedings of the 14th ACM international conference on Information and knowledge management
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Mining Complex Time-Series Data by Learning Markovian Models
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Personalized query expansion for the web
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Using query contexts in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Studying the use of popular destinations to enhance web search interaction
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Adapting document ranking to users’ preferences using click-through data
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
In the Mood to Click? Towards Inferring Receptiveness to Search Advertising
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Inferring search behaviors using partially observable Markov (POM) model
Proceedings of the third ACM international conference on Web search and data mining
Mining advertiser-specific user behavior using adfactors
Proceedings of the 19th international conference on World wide web
Context-aware ranking in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Transitive history-based query disambiguation for query reformulation
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Exploring online social activities for adaptive search personalization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Multidimensional mining of large-scale search logs: a topic-concept cube approach
Proceedings of the fourth ACM international conference on Web search and data mining
Sparse hidden-dynamics conditional random fields for user intent understanding
Proceedings of the 20th international conference on World wide web
Context-sensitive query auto-completion
Proceedings of the 20th international conference on World wide web
iMecho: a context-aware desktop search system
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Social analytics for personalization in work environments
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Predicting Next Search Actions with Search Engine Query Logs
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
ClickRank: Learning Session-Context Models to Enrich Web Search Ranking
ACM Transactions on the Web (TWEB)
Folksonomy query suggestion via users' search intent prediction
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Are web users really Markovian?
Proceedings of the 21st international conference on World Wide Web
Data Mining and Knowledge Discovery
HMM-CARe: Hidden Markov Models for context-aware tag recommendation in folksonomies
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Improving search via personalized query expansion using social media
Information Retrieval
MapReduce algorithms for big data analysis
Proceedings of the VLDB Endowment
An architecture for personalized health information retrieval
Proceedings of the 2012 international workshop on Smart health and wellbeing
Proceedings of the sixth ACM international conference on Web search and data mining
Task-aware query recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Discovering temporal hidden contexts in web sessions for user trail prediction
Proceedings of the 22nd international conference on World Wide Web companion
A vlHMM approach to context-aware search
ACM Transactions on the Web (TWEB)
Intent models for contextualising and diversifying query suggestions
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Mining search and browse logs for web search: A Survey
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Session modeling to predict online buyer behavior
Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media
Modeling search processes using hidden states in collaborative exploratory web search
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Integrating collaborative context information with social media: a study of user perceptions
Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration
Fast topic discovery from web search streams
Proceedings of the 23rd international conference on World wide web
Hi-index | 0.00 |
Capturing the context of a user's query from the previous queries and clicks in the same session may help understand the user's information need. A context-aware approach to document re-ranking, query suggestion, and URL recommendation may improve users' search experience substantially. In this paper, we propose a general approach to context-aware search. To capture contexts of queries, we learn a variable length Hidden Markov Model (vlHMM) from search sessions extracted from log data. Although the mathematical model is intuitive, how to learn a large vlHMM with millions of states from hundreds of millions of search sessions poses a grand challenge. We develop a strategy for parameter initialization in vlHMM learning which can greatly reduce the number of parameters to be estimated in practice. We also devise a method for distributed vlHMM learning under the map-reduce model. We test our approach on a real data set consisting of 1.8 billion queries, 2.6 billion clicks, and 840 million search sessions, and evaluate the effectiveness of the vlHMM learned from the real data on three search applications: document re-ranking, query suggestion, and URL recommendation. The experimental results show that our approach is both effective and efficient.