Online information retrieval: concepts, principles, and techniques
Online information retrieval: concepts, principles, and techniques
A statistical interpretation of term specificity and its application in retrieval
Document retrieval systems
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Modern Information Retrieval
Automatic query wefinement using lexical affinities with maximal information gain
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
The Journal of Machine Learning Research
Personalized Web Search For Improving Retrieval Effectiveness
IEEE Transactions on Knowledge and Data Engineering
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurately interpreting clickthrough data as implicit feedback
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
Usage patterns of collaborative tagging systems
Journal of Information Science
Anonymous personalization in collaborative web search
Information Retrieval
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
Personalized query expansion for the web
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring social annotations for information retrieval
Proceedings of the 17th international conference on World Wide Web
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Matching task profiles and user needs in personalized web search
Proceedings of the 17th ACM conference on Information and knowledge management
Tag data and personalized information retrieval
Proceedings of the 2008 ACM workshop on Search in social media
Proceedings of the 18th international conference on World wide web
Toward personalized query expansion
Proceedings of the Second ACM EuroSys Workshop on Social Network Systems
Semi-supervised classification using local and global regularization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Social Tagging in Query Expansion: A New Way for Personalized Web Search
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
The Probabilistic Relevance Framework: BM25 and Beyond
Foundations and Trends 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
Improving social bookmark search using personalised latent variable language models
Proceedings of the fourth ACM international conference on Web search and data mining
Collaborative pseudo-relevance feedback
Expert Systems with Applications: An International Journal
Personalised Information Retrieval: survey and classification
User Modeling and User-Adapted Interaction
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Social tagging systems have gained increasing popularity as a method of annotating and categorizing a wide range of different web resources. Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional information retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics only showed limited improvements. This paper proposes a novel query expansion framework based on individual user profiles mined from the annotations and resources the user has marked. The underlying theory is to regularize the smoothness of word associations over a connected graph using a regularizer function on terms extracted from top-ranked documents. The intuition behind the model is the prior assumption of term consistency: the most appropriate expansion terms for a query are likely to be associated with, and influenced by terms extracted from the documents ranked highly for the initial query. The framework also simultaneously incorporates annotations and web documents through a Tag-Topic model in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. Hence, the proposed approach significantly benefits personalized web search by leveraging users' social media data.