Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty [article]

Junyang Jiang and Deqing Yang and Yanghua Xiao and Chenlu Shen
2020 arXiv   pre-print
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
more » ... presentations 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.
arXiv:2006.10932v1 fatcat:4wjxcknbbbgdhhbgtzwawx73ny