A software system for buzz-based recommendations
Hill Nguyen, Nish Parikh, Neel Sundaresan · 2008
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.