A comparative study of the conventional item-based collaborative filtering and the Slope One algorithms for recommender systems

Abstract: Recommender systems are an important research topic in todays society as the amount of data increases across the globe. In order for commercial systems to give their users good and personalized recommendations on what data may be of interest to them in an effective manner, such a system must be able to give recommendations quickly and scale well as data increases. The purpose of this study is to evaluate two such algorithms with this in mind.  The two different algorithm families tested are classified as item-based collaborative filtering but work very differently. It is therefore of interest to see how their complexities affect their performance, accuracy as well as scalability. The Slope One family is much simpler to implement and proves to be equally as efficient, if not even more efficient than the conventional item-based ones. Both families do require a precomputation stage before recommendations are possible to give, this is the stage where Slope One suffers in comparison to the conventional item-based one. The algorithms are tested using Lenskit, on data provided by GroupLens and their MovieLens project.