Helpfulness-Aware Matrix Factorization for Cross-Category Service Recommendations

Matrix factorization is a popular method for building recommendation models. On e-commerce platforms, this method makes predictions of product ratings for goods which have not been rated. Similarly, in service computing, service rating platforms have been proposed to help users to select services. The idea is constantly evolving and the proposed models are often only validated by synthetic data. Existing recommendation systems rarely consider the fact that while customer feedbacks are usually valuable, some are questionable. Hence, how objective the given ratings are is an important factor. By considering the contribution of each rating according to its helpfulness and its objectivity, this paper proposes a platform that can make precise and cross-category recommendations. We exploit the parallelism between service and product recommendations to validate our proposed model by real-world data.