Learning Intrinsic and Extrinsic Intentions for Cold-start Recommendation with Neural Stochastic Processes

User behavior data in recommendation are driven by the complex interactions of many intentions behind the user's decision making process. However, user behavior data tends to be sparse because of the limited user response and the vase combinations of users and items, which result in unclear user intentions and suffer from cold-start problem. The intentions are highly compound, and may range from high-level ones that govern user's intrinsic interests and realize the underlying reasons behind the user's decision making processes, to low-level one that characterize a user's extrinsic preference when executing intention to specific items. In this paper, we propose an intention neural process model (INP) for user cold-start recommendation (i.e., user with very few historical interactions), a novel extension of the neural stochastic process family using a general meta learning strategy with intrinsic and extrinsic intention learning for robust user preference learning. By regarding the recommendation process for each user as a stochastic process, INP defines distributions over functions, is capable of rapid adaptation to new users. Our approach learns intrinsic intentions by inferring the high-level concepts associated with user interests or purposes, while capturing the target preference of a user by performing self-supervised intention matching between historical items and target items in a disentangled latent space. Extrinsic intentions are learned by simultaneously generating the point-wise implicit feedback data and creates the pair-wise ranking list by sufficient exploiting both interacted and non-interacted items for each user. Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines on cold-start recommendation.