MetaGA: Metalearning With Graph-Attention for Improved Long-Tail Item Recommendation

The recommendation of long-tail items has been a persistent issue in recommender system research. The primary reason for this problem is that the model cannot learn better item features due to the lack of interactive record data of tail items, which leads to a decline in the model's recommendation performance. Existing methods transfer the features of the head items to the tail items, thereby ignoring their differences and failing to produce a satisfactory recommendation effect. To address the issue, we propose a novel recommendation model called MetaGA based on metalearning. The MetaGA model obtains initial parameters from head items through metalearning and fine-tunes model parameters during the learning process of tail item features. Additionally, it employs a graph convolutional network and attention mechanism to enhance tail data and reduce the difference between head and tail data. Through the above two steps, the model utilizes the abundant data of the head items to address the problem of sparse data of the tail items, resulting in improved recommendation performance. We conducted extensive experiments on three real-world datasets, and the results demonstrate that our proposed MetaGA model significantly outperforms other state-of-the-art baselines for tail item recommendation.