Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining broad latent query aspects from search sessions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Document recommendation in social tagging services
Proceedings of the 19th international conference on World wide web
Clustering query refinements by user intent
Proceedings of the 19th international conference on World wide web
Building taxonomy of web search intents for name entity queries
Proceedings of the 19th international conference on World wide web
Inferring query intent from reformulations and clicks
Proceedings of the 19th international conference on World wide web
Learning search tasks in queries and web pages via graph regularization
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Behavior-driven clustering of queries into topics
Proceedings of the 20th ACM international conference on Information and knowledge management
Sequence clustering and labeling for unsupervised query intent discovery
Proceedings of the fifth ACM international conference on Web search and data mining
An exploration of improving collaborative recommender systems via user-item subgroups
Proceedings of the 21st international conference on World Wide Web
Mining query subtopics from search log data
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
The wisdom of advertisers: mining subgoals via query clustering
Proceedings of the 21st ACM international conference on Information and knowledge management
More than relevance: high utility query recommendation by mining users' search behaviors
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the sixth ACM international conference on Web search and data mining
Learning query and document similarities from click-through bipartite graph with metadata
Proceedings of the sixth ACM international conference on Web search and data mining
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The problem of learning user search intents has attracted intensive attention from both industry and academia. However, state-of-the-art intent learning algorithms suffer from different drawbacks when only using a single type of data source. For example, query text has difficulty in distinguishing ambiguous queries; search log is bias to the order of search results and users' noisy click behaviors. In this work, we for the first time leverage three types of objects, namely queries, web pages and Wikipedia concepts collaboratively for learning generic search intents and construct a heterogeneous graph to represent multiple types of relationships between them. A novel unsupervised method called heterogeneous graph-based soft-clustering is developed to derive an intent indicator for each object based on the constructed heterogeneous graph. With the proposed co-clustering method, one can enhance the quality of intent understanding by taking advantage of different types of data, which complement each other, and make the implicit intents easier to interpret with explicit knowledge from Wikipedia concepts. Experiments on two real-world datasets demonstrate the power of the proposed method where it achieves a 9.25% improvement in terms of NDCG on search ranking task and a 4.67% enhancement in terms of Rand index on object co-clustering task compared to the best state-of-the-art method.