基于时序特征的图卷积协同过滤推荐算法

基于图卷积神经网络的协同过滤推荐算法框架是目前最先进的推荐算法框架，该框架在用户-项目交互嵌入向量的特征学习中并未关注交互发生的时序性，但实际情况中，用户-项目交互普遍具有明显的时序特征，且是影响推荐性能的重要因素。因此，本文提出一种基于时序特征的图卷积协同过滤推荐算法，重做多个数据集，保留数据集时序特征等原始信息，总结归纳数据集中用户-项目交互的历史时序信息，并对其进行参数化处理，作为重要特征输入到图卷积网络模型训练的高阶协同信号传递中。在三个公开的官方数据集Gowalla,Yelp和Amazon-book上设置多组消融性实验，采用公认的评价指标ndcg和recall对推荐算法性能进行评价，实验结果证明，在同样参数设置下，基于时序特征的图卷积协同过滤推荐算法性能超越了现有同类型图卷积协同过滤推荐算法，验证了时序特征对提升推荐效果的积极作用，提高了模型训练效率和预测命中率，更加高效地解决网络信息过载问题，满足了更高的应用需求。

Graph convolution collaborative filtering recommendation algorithm based on the time series features

The collaborative filtering recommendation algorithm framework based on graph convolutional neural network is the most advanced recommendation algorithm framework at present. The framework does not pay attention to the timing of interaction occurrence in the feature learning of user-item interaction embedding vector, but in actual situations, users- Item interaction generally has obvious timing characteristics and is an important factor affecting recommendation performance. Based on this, a graph convolution collaborative filtering recommendation algorithm based on time series features is proposed, which redo multiple data sets, retain the original information of the data sets, especially the time series features, and summarize the historical time series information of user-item interaction in the data set. It is parameterized and put as an important feature input to the high-order cooperative signal transmission of graph convolutional network model training. Set up multiple sets of ablative experiments on three publicly available official datasets-Gowalla, Yelp and Amazon-book, and use recognized evaluation indicators-ndcg and recall to evaluate the performance of the recommendation algorithm. The experimental results show that under the same parameter Settings, the figure convolution collaborative filtering recommendation algorithm based on temporal characteristics performance beyond the existing same type figure convolution, collaborative filtering recommendation algorithm to verify the timing characteristics are recommended to improve effect of the positive role, improve the efficiency of model training and prediction, shooting more efficiently solve the problem of network information overload, to satisfy the higher application requirements.