On generating all maximal independent sets
Information Processing Letters
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
On the learnability and usage of acyclic probabilistic finite automata
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Fast computation of low rank matrix approximations
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Using mixture models for collaborative filtering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Using the wisdom of the crowds for keyword generation
Proceedings of the 17th international conference on World Wide Web
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
Learning about the world through long-term query logs
ACM Transactions on the Web (TWEB)
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Releasing search queries and clicks privately
Proceedings of the 18th international conference on World wide web
Query clustering using click-through graph
Proceedings of the 18th international conference on World wide web
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving classification accuracy using automatically extracted training data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Catching the drift: learning broad matches from clickthrough data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Substitutes or complements: another step forward in recommendations
Proceedings of the 10th ACM conference on Electronic commerce
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Clustering query refinements by user intent
Proceedings of the 19th international conference on World wide web
Jigs and lures: associating web queries with structured entities
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Interactive pattern mining on hidden data: a sampling-based solution
Proceedings of the 21st ACM international conference on Information and knowledge management
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Recommendation engines today suggest one product to another, e.g., an accessory to a product. However, intent to buy often precedes a user's appearance in a commerce vertical: someone interested in buying a skateboard may have earlier searched for {varial heelflip}, a trick performed on a skateboard. This paper considers how a search engine can provide early warning of commercial intent. The naive algorithm of counting how often an interest precedes a commercial query is not sufficient due to the number of related ways of expressing an interest. Thus, methods are needed for finding sets of queries where all pairs are related, what we call a query community, and this is the technical contribution of the paper. We describe a random model by which we obtain relationships between search queries and then prove general conditions under which we can reconstruct query communities. We propose two complementary approaches for inferring recommendations that utilize query communities in order to magnify the recommendation signal beyond what an individual query can provide. An extensive series of experiments on real search logs shows that the query communities found by our algorithm are more interesting and unexpected than a baseline of clustering the query-click graph. Also, whereas existing query suggestion algorithms are not designed for making commercial recommendations, we show that our algorithms do succeed in forecasting commercial intent. Query communities increase both the quantity and quality of recommendations.