Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.
翻译:建议系统是缓解信息超负荷问题的关键工具,目的是通过分析观察到的用户-项目关系,预测来自数百万候选人的用户偏好项目,目的是通过分析观察到的用户-项目关系,预测用户偏好项目。 关于解决推荐系统遇到的偏狭和冷启动问题,利用侧侧信息和知识来丰富所观察到的建议信息,发现隐藏的(间接)用户-项目关系,最近证明是大有希望的;其业绩在很大程度上取决于建议模型在面临高度复杂和庞大的侧面信息和知识的情况下的可缩放性。在有效利用复杂和大型的侧面信息和知识方面取得重大进展,研究图形嵌入技术是一个主要议题。用图形嵌入技术来改进推荐系统,有助于执行直接根据图表表层分析执行的传统建议,并对这些年进行广泛研究。 本文系统地追溯图形嵌入基于双面图、一般图表和知识图表技术的嵌入性建议,并提议一个基于此的一般设计管道。此外,将一些基于代表性图表的嵌入式建议模型与一些最常用的常规建议模型模型进行比较。在模拟中,将常规的嵌入技术的嵌入技术,将隐含式格式的嵌入将常规模型的模型作为基础建议,将这些常规模型的模型的变化结构结构结构的变列的总体建议,将这些常规模型作为基础建议作为基础,将这些常规模型的缩化的缩化的模型作为这些导成一个基础的建议。