Convolutional Gaussian Embeddings for
Personalized Recommendation with Uncertainty
Abstract
Most of existing embedding based recommendation models use embeddings (vectors) corresponding to a single fixed point in low-dimensional
space, to represent users and items. Such embeddings fail to precisely represent the users/items
with uncertainty often observed in recommender
systems. Addressing this problem, we propose a
unified deep recommendation framework employing Gaussian embeddings, which are proven adaptive to uncertain preferences exhibited by some
users, resulting in better user representations and
recommendation performance. Furthermore, our
framework adopts Monte-Carlo sampling and convolutional neural networks to compute the correlation between the objective user and the candidate item, based on which precise recommendations are achieved. Our extensive experiments on
two benchmark datasets not only justify that our
proposed Gaussian embeddings capture the uncertainty of users very well, but also demonstrate its
superior performance over the state-of-the-art recommendation models