  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.
