FRAC+: A distributed collaborative filtering model for client/server architectures

This paper describes a new way of implementing an intelligent web caching service, based on an analysis of usage. Since the cache sizes in software are limited, and the search for new information is time-consuming, it becomes interesting to automate the process of selecting the most relevant items for each user. We propose a new model (FRAC+), based on a decentralized collaborative filtering algorithm (FRAC) and a behavior modeling process. Our solution is particularly designed to address the issues of data sparsity, privacy and scalability. We consider the situation where the set of users is relatively stable, whereas the set of items may vary considerably from an execution to another. We furthermore assume that the number of users is much more important than the number of items. We argue that a user-based approach seems to be more suitable to address the aforementioned issues. We present a performance assessment of our technique called FRAC in terms of computation time and prediction relevancy, the two most reliable performance criteria in the industrial context we are involved in. This work has been implemented within the ASTRA satellite website broadcasting service.