Distributed User-Based Collaborative Filtering on an Opportunistic Network

This paper presents a novel collaborative filtering recommender system based on an opportunistic distributed network. Collaborative filtering algorithms are widely used in many online systems. Often, the computation of these recommender systems is performed on a central server, controlled by the provider, requiring constant internet connection for gathering and computing data. However, in many scenarios, such constraints cannot be guaranteed or may not even be desired. This work proposes a recommendation engine where users share information via an opportunistic network independent of a dedicated internet connection. In this framework, each node is responsible for gathering information from nearby nodes and calculating its own recommendations. Using a centralized collaborative filtering recommender as a baseline, we evaluate three simulated scenarios composed by different movement speeds and data exchange parameters. Our results show that in a relatively short time, an opportunistic distributed recommender systems can achieve results similar to a traditional central system. Our analysis shows that the speed at which the opportunistic recommender system stabilizes depends on several factors including density of the users, movement speed and patterns of the users, and transmission strategies.