Recommendation feedback-based dynamic adaptive training for efficient social item recommendation

For the application of social item recommendation, how to effectively dig out the implicit relationships between different items plays a crucial role in its performance. However, existing social item recommendation systems constructed their item graphs using a static method based on item features. Considering the fact that most items, such as live streams, can hardly be characterized with limited number of feature tags in reality, the static construction methods make it hard to accurately grasp the underlying item–item relationships. To address the problem, we propose an item graph generation method based on Recommendation Feedback and Dynamic Adaptive Training (RFDAT) to achieve an efficient social item recommendation. Specifically, a multi-task learning technique is leveraged to concurrently predict the item graph and user–item interaction graph, allowing the recommendation task itself to directly participate in the dynamic construction process of the item graph, which is adaptively constructed based on feedback from recommendation results iteratively during the training procedure. Compared with the static construction methods, this allows us to fully explore item–item relationships and item feature representations, therefore improving recommendation accuracy. Furthermore, a lightweight graph convolutional denoising and fusion method based on Laplacian smoothing filter is employed to achieve deep interaction and fusion among multi-graph features, and effectively mitigate the influence of noise in the process of feature learning. Finally, extensive experimental results on four public datasets show that compared with eight state-of-the-art methods, our proposed method achieves improvements of 4.97%, 2.90%, 2.03%, and 4.82% in the important evaluation metric NDCG@10 on Yelp, Ciao, LastFM, and Douban datasets, respectively. It also illustrates very competitive performance against these baselines in the recommendation accuracy for cold users and the recommendation rate for cold items.