A hybrid recommendation scheme for delay-tolerant networks: The case of digital marketplaces

Abstract

Recommender systems are widely-adopted by numerous popular e-commerce sites, such as Amazon and E-bay, to help users find products that they might like. Although much has been achieved in the area, most recommender systems are designed to work on top of centralized platforms that are traditionally supported by fixed infrastructure like the Internet. Hence, additional work is warranted to examine the applicability and performance of recommender systems in challenging environments that are characterized by dynamic network topology and variable transmission delays. This study deals with the design of a recommender system that is compatible in a delay-tolerant network where communication is supported by opportunistic encounters between participating nodes. The proposed approach combines collaborative filtering and content-based filtering techniques to generate rating predictions for users. To make the system more tolerant against interruptions, each node maintains a local recommender that generates predictions using user profiles that are obtained through opportunistic exchanges over a clustered topology. Simulation results indicate that the proposed approach is able to improve coverage while alleviating the cold-start problem.