Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty
This work addresses uncertainty in user preferences for recommender systems, offering a novel method that enhances representation and performance.
The paper tackled the problem of representing uncertain user preferences in recommender systems by proposing a unified deep recommendation framework using Gaussian embeddings, which improved recommendation performance over state-of-the-art models on two benchmark datasets.
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.