基于深度学习的欧几里得嵌入的推荐算法

Recommendation Algorithm of Euclidean Embedding Based on Deep Learning

推荐系统为用户推荐用户可能感兴趣的物品,可以有效地减轻信息过载。基于欧几里得嵌入的协同过滤方法将用户和物品映射到统一的隐藏空间中,是构建推荐系统的重要方法之一。然而,传统的基于欧几里得嵌入的推荐方法仅考虑用户和物品隐藏特征向量之间低阶交互,不能有效建模现实世界中用户和物品的复杂交互行为。本文提出基于深度学习的欧几里得嵌入的协同过滤算法,利用深度学习技术学习用户和物品隐藏特征向量之间的高阶、非线性交互函数,建模用户和物品之间复杂交互行为。在真实数据集上的实验结果表明,基于深度学习的欧几里得嵌入的协同过滤算法性能优于传统协同过滤算法。

Recommender systems can effectively reduce the information overload by recommending items that users may be interested in.Euclidean-embedding-based collaborative filtering methods map users and items to a unified latent space,which is one of the most important methods to build a recommender system.However,traditional Euclidean-embedding-based collaborative filtering methods only consider the low-order interaction between user latent feature vectors and item latent feature vectors,and cannot efficiently model the complex interaction behavior between users and items in the real world.In this paper,we propose a deep-Euclideanembedding-based collaborative filtering algorithm,which utilizes deep learning technology to learn the highorder and nonlinear interaction function between user latent feature vectors and item latent feature vectors.This can model the complex interaction behavior between users and items.Experimental results on real-world datasets show that our proposed algorithm outperforms traditional collaborative filtering algorithms.