Collaborative filtering (CF) is successfully applied to recommendation system by digging the latent features of users and items. However, conventional CF-based models usually suffer from the sparsity of rating matrices which would degrade model’s recommendation performance. To address this sparsity problem, auxiliary information such as labels are utilized. Another approach of recommendation system is content-based model which can’t be directly integrated with CF-based model due to its inherent characteristics. Considering that deep learning algorithms are capable of extracting deep latent features, this paper applies Stack Denoising Auto Encoder (SDAE) to content-based model and proposes LCF(Deep Learning for Collaborative Filtering) algorithm by combing CF-based model which fuses label features. Experiments on real-world data sets show that DLCF can largely overcome the sparsity problem and significantly improves the state of art approaches.
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机译：通过挖掘用户和项目的潜在特征，协作过滤（CF）成功地应用于推荐系统。但是，传统的基于CF的模型通常会遭受评分矩阵的稀疏性，这会降低模型的推荐性能。为了解决该稀疏性问题，利用了诸如标签的辅助信息。推荐系统的另一种方法是基于内容的模型，由于其固有的特性，因此无法与基于CF的模型直接集成。考虑到深度学习算法能够提取深度潜在特征，本文将Stack Denoising Auto Encoder（SDAE）应用于基于内容的模型，并通过结合融合标签特征的基于CF的模型提出LCF（协同过滤深度学习）算法。在现实世界数据集上的实验表明，DLCF可以在很大程度上克服稀疏性问题并显着改善现有技术的状态。
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