Cold Start Recommendation Based on Attribute-Fused Singular Value Decomposition

Abstract

Collaborative filtering plays an important role in promoting the service recommendation ecosystem, and the matrix decomposition technology has been proven to be one of the most effective recommendation methods. However, the traditional collaborative filtering algorithm has great shortcomings in the recommendation of cold start items, especially the emergence of new items will be largely ignored. This not only has a very bad impact on the development of the item, but also greatly reduces the diversity of the recommendation system. The rise of mobile devices has also brought a large number of mobile applications, and these emerging applications need to be promoted in order to maintain the robustness of the application system. In order to solve this problem, we propose a method of combining the attribute information of the item with the historical rating matrix to predict the potential preferences of the user. It combines the attribute and time information into a matrix decomposition model. By testing our method on the movielens and the climbed JD dataset, the experimental results show that, compared with the baseline method, the proposed method achieves a significant improvement in recommendation accuracy. Therefore, this method is an effective way to solve the cold start problem of new items.