Improving the Performance of Cold-Start Recommendation by Fusion of Attention Network and Meta-Learning

Abstract

The cold-start problem has always been a key challenge in the recommendation research field. As a popular method to learn a learner that can rapidly adapt to a new task through a small number of updates, meta-learning is considered to be a feasible algorithm to reduce the error of cold-start recommendation. However, meta-learning does not take the diverse interests of users into account, which limits the performance improvement in cold-start scenarios. In this paper, we proposed a new model for a cold-start recommendation, which combines the attention mechanism and meta learning. This method enhances the ability of modeling the personalized user interest by learning the weights between users and items based on the attention mechanism and then improves the performance of the cold-start recommendation. We validated the model with two publicly available datasets in the recommendation field. Compared with the three benchmark methods, the proposed model reduces the mean absolute error by at least 2.3% and the root mean square error of 2.5%.