Empirical analysis of attribute-aware recommendation algorithms with variable synthetic data

Recommender Systems (RS) have helped achieving success in E-commerce. Delving better RS algorithms has been an ongoing research. However, it has always been difficult to find adequate datasets to help evaluating RS algorithms. Public data suitable for such kind of evaluation is limited, especially for data containing content information (attributes). Previous researches have shown that the performance of RS rely on the characteristics and quality of datasets. Although, a few others have conducted studies on synthetically generated data to mimic the user-product datasets, datasets containing attributes information are rarely investigated. In this paper, we review synthetic datasets used in RS and present our synthetic data generator that considers attributes. Moreover, we conduct empirical evaluations on existing hybrid recommendation algorithms and other state-of-the-art algorithms using these synthetic data and observe the sensitivity of the algorithms when varying qualities of attribute data are applied to the them.