Scalable and near real-time burst detection from eCommerce queries
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Inferring semantic query relations from collective user behavior
Proceedings of the 17th ACM conference on Information and knowledge management
Query suggestion for E-commerce sites
Proceedings of the fourth ACM international conference on Web search and data mining
User behavior in zero-recall ecommerce queries
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Rewriting null e-commerce queries to recommend products
Proceedings of the 21st international conference companion on World Wide Web
A multi-agent recommender system for supporting device adaptivity in e-Commerce
Journal of Intelligent Information Systems
Early detection of buzzwords based on large-scale time-series analysis of blog entries
Proceedings of the 23rd ACM conference on Hypertext and social media
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In this paper, we describe a buzz-based recommender system based on a large source of queries in an eCommerce application. The system detects bursts in query trends. These bursts are linked to external entities like news and inventory information to find the queries currently in-demand which we refer to as buzz queries. The system follows the paradigm of limited quantity merchandising, 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 improving activity and stickiness on the site. A semantic neighborhood of the chosen buzz query is selected and appropriate recommendations are made on products that relate to this neighborhood.