Recent work has shown that collaborative filter-based recommender systems can
be improved by incorporating side information, such as natural language
reviews, as a way of regularizing the derived product representations.
Motivated by the success of this approach, we introduce two different models of
reviews and study their effect on collaborative filtering performance. While
the previous state-of-the-art approach is based on a latent Dirichlet
allocation (LDA) model of reviews, the models we explore are neural network
based: a bag-of-words product-of-experts model and a recurrent neural network.
We demonstrate that the increased flexibility offered by the product-of-experts
model allowed it to achieve state-of-the-art performance on the Amazon review
dataset, outperforming the LDA-based approach. However, interestingly, the
greater modeling power offered by the recurrent neural network appears to
undermine the model's ability to act as a regularizer of the product
representations.

通过引入自然语言评论作为优化产品表示的一种方式，将旁路信息融入基于协同过滤的推荐系统可以提高其性能表现；本文提出两种不同类型的评论模型，包括基于神经网络的词袋专家模型和循环神经网络模型，并证明前者的模型灵活性表现出了更高的性能表现，超越了基于 LDA 的方法。