Search the web x.0: mining and recommending web-mediated processes
Proceedings of the third ACM conference on Recommender systems
User profiling and personalized information delivery on the static and mobile web
Proceedings of the eleventh international workshop on Web information and data management
Constructing concept relation network and its application to personalized web search
Proceedings of the 14th International Conference on Extending Database Technology
Applications of concept relation network to web search
Proceedings of the 1st International Workshop on Linked Web Data Management
Context-aware search personalization with concept preference
Proceedings of the 20th ACM international conference on Information and knowledge management
A framework for personalizing web search with concept-based user profiles
ACM Transactions on Internet Technology (TOIT)
Dynamic agglomerative-divisive clustering of clickthrough data for collaborative web search
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Personalized document clustering with dual supervision
Proceedings of the 2012 ACM symposium on Document engineering
Efficient semantic network construction with application to PubMed search
Knowledge-Based Systems
Concept based query recommendation
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Discovering tasks from search engine query logs
ACM Transactions on Information Systems (TOIS)
Journal of Information Science
Expert Systems with Applications: An International Journal
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The exponential growth of information on the Web has introduced new challenges for building effective search engines. A major problem of web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new two-phase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors' knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.