  In 2010, Web users ordered, only in Amazon, 73 items per second and massively
contribute reviews about their consuming experience. As the Web matures and
becomes social and participatory, collaborative filters are the basic
complement in searching online information about people, events and products.
In Web 2.0, what connected consumers create is not simply content (e.g.
reviews) but context. This new contextual framework of consumption emerges
through the aggregation and collaborative filtering of personal preferences
about goods in the Web in massive scale. More importantly, facilitates
connected consumers to search and navigate the complex Web more effectively and
amplifies incentives for quality. The objective of the present article is to
jointly review the basic stylized facts of relevant research in recommendation
systems in computer and marketing studies in order to share some common
insights. After providing a comprehensive definition of goods and Users in the
Web, we describe a classification of recommendation systems based on two
families of criteria: how recommendations are formed and input data
availability. The classification is presented under a common minimal matrix
notation and is used as a bridge to related issues in the business and
marketing literature. We focus our analysis in the fields of one-to-one
marketing, network-based marketing Web merchandising and atmospherics and their
implications in the processes of personalization and adaptation in the Web.
Market basket analysis is investigated in context of recommendation systems.
Discussion on further research refers to the business implications and
technological challenges of recommendation systems.
