近年来基于矩阵分解的协同过滤算法在评分预测上取得了显著成果，但仍未能很好地解决冷启动、数据稀疏等问题。因此，如何将评论信息引入推荐系统以缓解上述问题成为研究的热点之一。该文尝试基于深度学习来加强个性化推荐，提出将层叠降噪自动编码器（stacked denoising auto-encoder，SDAE）与隐含因子模型（latent factor model，LFM）相结合的混合推荐方法，综合考虑评论文本与评分，以提升推荐模型对潜在评分预测的准确性。在常用大规模公开Amazon数据集上进行的测试结果表明：与传统推荐模型相比，该文提出的方法可有效提高评分预测的准确性，性能提升最高可达64.43%。
Collaborative filtering based on matrix factorization has been very successful, while cold-start and data sparseness problems have not been well resolved. Hence, many studies have attempted to include review information into rating predictions. This paper presents a hybrid model that introduces deep learning into recommendation system with collaborative filtering. The algorithm combines a stacked denoising auto encoder (SDAE) with a latent factor model (LFM) to make use of both review and rating information to improve the rating predictions. Evaluations on a large, commonly used Amazon dataset show that this approach significantly improves the rating prediction accuracy in comparison with traditional models, with up to 64.43% better predictions.
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