Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
Scalable and near real-time burst detection from eCommerce queries
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
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In this paper, we present an outline of a software system for buzz-based recommendations. This system is based on a large source of queries in an eCommerce application. The buzz events are detected based on query bursts linked to external entities like news and inventory information. A semantic neighborhood of the chosen buzz query is selected and appropriate recommendations are made on products that relate to this neighborhood. The system follows the paradigm of limited quantity merchandizing, in the sense that on a per-day basis the system shows recommendations around a single buzz query with the intent of increasing user curiosity and promoting user activity and stickiness. The system demonstrates the deployment of an interesting application based on KDD principles applied to a high volume industrial context.