SamWalker: Social Recommendation with Informative Sampling Strategy

Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data. Only positive feedback are observed and the unobserved feedback can be attributed to two reasons: unknow or dislike. Existing methods address this challenge by treating all the un-observed data as negative (dislike) but downweight the confidence of these data. However, this treatment causes two problems: (1) Confidence weights of the unobserved data are usually assigned manually, which lack flexible and may create empirical bias in evaluating user's preference. (2) To handle massive volume of the unobserved feedback data, most of the existing methods rely on stochastic inference and data sampling strategies. However, since users are only aware of a very small fraction of items in a large dataset, it is difficult for existing samplers to select informative training instances in which the user really dislikes the item rather than does not know it. To address the above two problems, we propose a new recommendation method SamWalker that leverages social information to infer data confidence and guide the sampling process. By modeling data confidence with a social context-aware function, SamWalker can adaptively specify different weights to different data based on users' social contexts. Further, a personalized random-walk-based sampling strategy is developed to adaptively draw informative training instances, which can speed up gradient estimation and reduce sampling variance. Extensive experiments on three real-world datasets demonstrate the superiority of the proposed SamWalker method and its sampling strategy.