REPERIO: A Flexible Architecture for Recommendation in an Industrial Context

In this chapter, the authors describe Reperio, a flexible and generic industrial recommender system able to deal with several kinds of data sources (content-based, collaborative, social network) in the same framework and to work on multi-platforms (Web service in a multi-user mode and mobile device in a mono-user mode). The item-item matrix is the keystone of the architecture for its efficiency and flexibility properties. In the first part, the authors present core functionalities and requirements of recommendation in an industrial context. In the second part, they present the architecture of the system and the main issues involved in its development. In the last part, the authors report experimental results obtained using Reperio on benchmarks extracted from the Netflix Prize with different filtering strategies. To illustrate the interest and flexibility of the architecture, they also explain how it is possible to take into account, for recommendations, external sources of information. In particular, the authors show how to exploit user generated contents posted on social networks to fill the item-item matrix. The process proposed includes a step of opinion classification.

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