Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Investigating behavioral variability in web search
Proceedings of the 16th international conference on World Wide Web
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
Exploiting web search to generate synonyms for entities
Proceedings of the 18th international conference on World wide web
Exploiting web search engines to search structured databases
Proceedings of the 18th international conference on World wide web
Extracting structured information from user queries with semi-supervised conditional random fields
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Structured annotations of web queries
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Result enrichment in commerce search using browse trails
Proceedings of the fourth ACM international conference on Web search and data mining
Query relaxation for entity-relationship search
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Efficient query rewrite for structured web queries
Proceedings of the 20th ACM international conference on Information and knowledge management
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Recent work in commerce search has shown that understanding the semantics in user queries enables more effective query analysis and retrieval of relevant products. However, due to lack of sufficient domain knowledge, user queries often include terms that cannot be mapped directly to any product attribute. For example, a user looking for designer handbags might start with such a query because she is not familiar with the manufacturers, the price ranges, and/or the material that gives a handbag designer appeal. Current commerce search engines treat terms such as designer as keywords and attempt to match them to contents such as product reviews and product descriptions, often resulting in poor user experience. In this study, we propose to address this problem by reformulating queries involving terms such as designer, which we call modifiers, to queries that specify precise product attributes. We learn to rewrite the modifiers to attribute values by analyzing user behavior and leveraging structured data sources such as the product catalog that serves the queries. We first produce a probabilistic mapping between the modifiers and attribute values based on user behavioral data. These initial associations are then used to retrieve products from the catalog, over which we infer sets of attribute values that best describe the semantics of the modifiers. We evaluate the effectiveness of our approach based on a comprehensive Mechanical Turk study. We find that users agree with the attribute values selected by our approach in about 95% of the cases and they prefer the results surfaced for our reformulated queries to ones for the original queries in 87% of the time.