Task Similarity Aware Meta Learning for Cold-Start Recommendation

In recommender systems, content-based methods and meta-learning involved methods usually have been adopted to alleviate the item cold-start problem. The former consider utilizing item attributes at the feature level and the latter aim at learning a globally shared initialization for all tasks to achieve fast adaptation with limited data at the task level. However, content-based methods only focus on the similarity of item attributes, ignoring the relationships established by user interactions. And for tasks with different distributions, most meta-learning-based methods are difficult to achieve better performance under a single initialization. To address the limitations mentioned above and combine the strengths of both methods, we propose a Task Similarity Aware Meta-Learning (TSAML) framework from two aspects. Specifically, at the feature level, we simultaneously introduce content information and user-item relationships to exploit task similarity. At the task level, we design an automatic soft clustering module to cluster similar tasks and generate the same initialization for similar tasks. Extensive offline experiments demonstrate that the TSAML framework has superior performance and recommends cold items to preferred users more effectively than other state-of-the-art methods.