Generating inter-dependent data streams for recommender systems

Recommender systems are essential tools in modern e-commerce, streaming services, search engines, social networks and many other areas including the scientific community. However, lack of publicly available data hinders the development and evaluation of recommender algorithms. To address this problem, we propose a Generator of Inter-dependent Data Streams (GIDS), capable of generating multiple temporal and inter-dependent synthetic datasets of relational data. The generator is able to simulate a collection of time-changing data streams, helping to effectively evaluate a variety of recommender systems, data fusion algorithms and incremental algorithms. The evaluation using recommender and data fusion algorithms showed that our generator can successfully mimic real datasets in terms of statistical data properties, and achieved performance of recommender systems.