Fast and effective query refinement
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACM SIGIR Forum
The Journal of Machine Learning Research
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Mining anchor text for query refinement
Proceedings of the 13th international conference on World Wide Web
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture
The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Internet-scale collection of human-reviewed data
Proceedings of the 16th international conference on World Wide Web
Query Directed Web Page Clustering
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Learn from web search logs to organize search results
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query suggestion based on user landing pages
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised query segmentation using generative language models and wikipedia
Proceedings of the 17th international conference on World Wide Web
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Exploratory Search
Mining broad latent query aspects from search sessions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Web page clustering using heuristic search in the web graph
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
What you seek is what you get: extraction of class attributes from query logs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-aspect query summarization by composite query
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Extracting query facets from search results
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Many web-search queries serve as the beginning of an exploration of an unknown space of information, rather than looking for a specific web page. To answer such queries effectively, the search engine should attempt to organize the space of relevant information in a way that facilitates exploration. We describe the ASPECTOR system that computes aspects for a given query. Each aspect is a set of search queries that together represent a distinct information need relevant to the original search query. To serve as an effective means to explore the space, ASPECTOR computes aspects that are orthogonal to each other and to have high combined coverage. ASPECTOR combines two sources of information to compute aspects. We discover candidate aspects by analyzing query logs, and cluster them to eliminate redundancies. We then use a mass-collaboration knowledge base (e.g., Wikipedia) to compute candidate aspects for queries that occur less frequently and to group together aspects that are likely to be "semantically" related. We present a user study that indicates that the aspects we compute are rated favorably against three competing alternatives - related searches proposed by Google, cluster labels assigned by the Clusty search engine, and navigational searches proposed by Bing.