creators_name: Parikh, Nish creators_name: Sundaresan, Neel type: conference_item datestamp: 2009-04-06 19:14:40 lastmod: 2009-04-07 14:03:15 metadata_visibility: show title: Buzz-Based Recommender System ispublished: pub full_text_status: public pres_type: poster abstract: 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. date: 2009-04 pagerange: 1231-1231 event_title: 18th International World Wide Web Conference event_location: Madrid, Spain event_dates: April 20th-24th, 2009 event_type: conference refereed: TRUE citation: Parikh, Nish and Sundaresan, Neel (2009) Buzz-Based Recommender System. In: 18th International World Wide Web Conference, April 20th-24th, 2009, Madrid, Spain. document_url: http://www2009.eprints.org/196/1/p1231.pdf