We propose a J-NCF 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, which takes both implicit and explicit feedback, point-wise and pair-wise loss into account. Experiments on several real-word 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.
翻译:我们建议采用J-NCF系统推荐系统的方法。 J-NCF模式采用一个联合神经网络,将深层特征学习和深层互动模式与评级矩阵结合起来,将深层学习和深层互动模式与评级矩阵结合起来。深层特征学习提取提取特征特征展示,根据用户-项目评级矩阵,用户和具有深层学习结构的项目的深度学习结构展示特征。深层互动模型利用深层特征学习过程生成的特征展示作为投入,捕捉与深层神经网络的非线性用户-项目互动。 J-NCF模式通过联合培训使深层特征学习和深层互动模型进程相互优化,从而改进建议性能。此外,我们还设计一个新的优化损失功能,将隐含和明确的反馈、点对点和对对对点损失都考虑在内。 几个真字数据集的实验显示,J-NCF在最新技术方法上取得了显著的改进,在MovioLens 100K数据集上改进了8.4%,电影1MineLens 1MD数据集的模型增加了10 %,在亚马逊-10的州电影数据集上提高了10。 NCG10级的精确度的精确度的精确度也分别评估了我们14.42%的精确度的精确度。