Towards a Generic Architecture for Recommenders Benchmarking

With current growth of internet sales and content consumption, more research efforts are focusing on developing recommendation and personalization algorithms as a solution for the choice overload problem. In this paper, we first enumerate several state-of-the-art recommendation algorithms in order to highlight their main ideas and methodologies. Then, we propose a generic architecture for recommender systems benchmarking. Using the proposed architecture, we implement and evaluate several variants of existing recommendation algorithms and compare their results to our unified recommendation model. The experiments are conducted on a real world dataset in order to assess the genericity of our recommendation model and its quality. At the end, we conclude with some ideas for further development and research.