Joint Neural Collaborative Filtering for Recommender Systems

Joint Neural Collaborative Filtering for Recommender Systems

We propose a Joint Neural Collaborative Filtering (J-NCI-) method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modeling captures non-linear user-item interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. J-NCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization that takes both implicit and explicit feedback, pointwise and pair-wise loss into account.Experiments on several real-world datasets show significant improvements of J-NCF over state-of-the-art methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR@10. NDCG@10 improvements are 12.42%, 14.24%, and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of J-NCF. Our experiments show that the J-NCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to state of the art baselines.

我们为推荐系统提出了一种联合神经协同过滤（J - NCF）方法。J - NCF模型应用了一个联合神经网络，它将深度特征学习和深度交互建模与评分矩阵相结合。深度特征学习基于用户 - 项目评分矩阵，通过深度学习架构提取用户和项目的特征表示。深度交互建模使用深度特征学习过程生成的特征表示作为输入，通过深度神经网络捕捉非线性的用户 - 项目交互。J - NCF通过联合训练使深度特征学习和深度交互建模过程能够相互优化，从而提高推荐性能。此外，我们设计了一种新的用于优化的损失函数，该函数同时考虑了隐式和显式反馈以及逐点损失和成对损失。在几个真实世界数据集上的实验表明，J - NCF相较于最先进的方法有显著改进，在MovieLens 100K数据集上，HR@10指标提高了8.24%，在MovieLens 1M数据集上提高了10.81%，在Amazon Movies数据集上提高了10.21%。NDCG@10指标分别提高了12.42%、14.24%和15.06%。我们还进行了实验来评估J - NCF的可扩展性和敏感性。我们的实验表明，与最先进的基准相比，J - NCF模型在不活跃用户和不同程度的数据稀疏性情况下具有有竞争力的推荐性能。