document: Buzz-based recommender system 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 .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O