基于深度混合模型评分推荐

从用户-项目评分矩阵中学习用户对项目的个性化偏好,对于评分推荐来说至关重要。许多推荐方法如潜在因子模型,无法充分利用评分矩阵中的交互信息学到较好的个性化偏好而得到较差推荐效果。受深度学习中Wide and Deep模型应用于APP推荐启发,本文提出一种深度混合模型并命名为DeepHM用于评分推荐。与Wide and Deep模型相比,使用DeepWide和DNN部分重构Wide模型和Deep模型得到DeepHM,并且DeepWide和DNN部分共享交互信息输入。因此,DeepHM可以更有效地使用评分矩阵中的用户和项目的交互信息学到个性化偏好信息。DeepHM将评分推荐作为分类问题旨在提高推荐准确性。实验表明在公开的Movielens数据集上DeepHM算法相比现有的基于评分推荐模型具有更好的效果。

Rating Recommendation Based on Deep Hybrid Model

Learning individual preferences of users for items from user-item rating matrix is critical for rating recommendation. Many recommendation methods, such as the Latent Factor model, can not make full use of the interaction information from rating matrix to learning individual preferences, and achieve unsatisfying results. Inspired by wide and deep learning model of deep learning in APP recommendation, deep hybrid model is proposed and named DeepHM for rating recommendation. Compared with the wide and deep model, deep wide model and DNN model are used to reconstruct wide model and deep model, which can get DeepHM and make DeepHM become shared input to its deep wide and deep parts. Therefore, DeepHM uses interaction information of user and item from rating matrix more efficiently to obtain individual preferences information. Furthermore, DeepHM treats the rating recommendation as a multi-classification problem aiming to improve the accuracy of recommendation. Through comprehensive experiments on public Movielens datasets, it demonstrates that the efficiency of DeepHM based on rating recommendation is better than that of the existing models.