基于ranking的深度张量分解群组推荐算法

针对当前群组推荐研究中，对于用户偏好建模时大多忽略了群组偏好与个人偏好之间的相互影响以及建模初始化问题，提出了一种基于ranking的混合深度张量分解群组推荐算法（R-HDTF）。该算法首先利用基于深度降噪自动编码器的混合神经网络对群组、个人和项目等信息进行初始化；然后提出基于成对张量分解模型来捕获群组、个人和项目之间的相关关系；最后，采用BPR标准优化张量分解的损失函数，学习提出算法的参数。在真实数据集上的实验结果表明，该算法性能优于传统的主流群组推荐算法。

Ranking based hybrid deep tensor factorization model for group recommendation

When modeling users'' preferences, current researches of group recommendation usually ignored the mutual influence between group preference and individual preference and the problem of modeling initialization. To address these issues, this paper proposed a new group recommendation model called ranking based hybrid deep tensor factorization model, namely R-HDTF model. First of all, this paper developed a hybrid deep learning-based initialization method, which utilized deep denoising autoencoder to pre-train the initial values of the parameters for R-HDTF model. Then, it proposed a pairwise tensor factorization model to capture the correlation among group, individual and item. Finally, it used the Bayesian personalized ranking metric to optimize the loss objective function of tensor factorization and learn the parameters of the proposed model. Experimental results on real data sets show that the performance of the proposed algorithm outperforms traditional group recommendation algorithm.