From Tapestry to SVD: A Survey of the Algorithms That Power Recommender Systems

Abstract

This paper is a survey of the algorithms that power recommender systems. To start, the social and monetary relevance of recommender systems is outlined. Then we delve into the specifics of how the first recommender system, Tapestry, coined the idea of numerically defining customer similarity. Moving forward, we show how this central concept of similarity is re-hashed in present day recommender systems, namely that of Amazon.com. Specifically, we examine the complexity of a user-based approach in a large scale system such as Amazon's, identify its weaknesses, and see how these weaknesses are overcome using an item-based approach. The last component of this paper focuses on the Netflix Prize™ and investigates the single most important algorithm in the contest so far: an incremental approach to finding the singular value decomposition (SVD) of a mostly-blank matrix.