Doubly Intention Learning for Cold-start Recommendation with Uncertainty-aware Stochastic Meta Process

The cold-start recommendation has been one of the most central problems in online platforms where new users or items arrive continuously. Although existing meta-learning based models with globally sharing knowledge show good performance in most cold-start scenarios, the ability to handle challenges on intention heterogeneity and prediction uncertainty is missing, and these two challenges are particularly evident in cold-start scenarios with fewer interaction data. To tackle the above challenges, in this paper, we present an uncertainty-aware Stochastic Meta Process with Doubly Intention learning (DISMP) for the cold-start recommendation, which has promising properties in uncertainty quantification. With the aid of the meta-learning stochastic process, DISMP can store general knowledge by capturing the relevance of different user-item pairs in terms of intentions and concepts, which is capable of rapid adaptation to new users and items. Furthermore, intentions with general and specific levels are extracted by doubly distinguishing the role of latent variables, which is able to capture the dependencies across different types of intentions and concepts. Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines on cold-start recommendations with different perspectives.