标题： 基于标签一致受限玻尔兹曼机的协同过滤 显示英文标题

标题： Collaborative Filtering with Label Consistent Restricted Boltzmann Machine

摘要： 利用受限玻尔兹曼机（RBM）进行协同过滤的可能性已经为人所知大约十年了。 然而，自2007年以来，关于这一主题的研究几乎很少。 这项工作重新审视了RBM在推荐系统中的应用。 基于RBM的协同过滤仅使用了评分信息；这是一个无监督架构。 这项工作通过利用用户人口统计信息和项目元数据来引入监督。 从表示层到标签（元数据）学习了一个网络。 提出的标签一致RBM公式显著改进了现有的基于RBM的方法，并取得了与最先进的潜在因子模型相当的结果。 显示英文摘要

摘要： The possibility of employing restricted Boltzmann machine (RBM) for collaborative filtering has been known for about a decade. However, there has been hardly any work on this topic since 2007. This work revisits the application of RBM in recommender systems. RBM based collaborative filtering only used the rating information; this is an unsupervised architecture. This work adds supervision by exploiting user demographic information and item metadata. A network is learned from the representation layer to the labels (metadata). The proposed label consistent RBM formulation improves significantly on the existing RBM based approach and yield results at par with the state-of-the-art latent factor based models.