用物品特征增强图表示，提升会话推荐精度
Unsupervised Graph Embeddings for Session-based Recommendation with Item Features
- 将物品特征融入图卷积网络构建共现图
- 在三个数据集上使MRR@20提升最高达12.79%
- 适合想改进会话推荐性能的工程师
在会话推荐系统中，预测基于用户当前会话的历史行为。现有先进方法要么使用图神经网络建模会话图，要么通过物品特征挖掘会话相似性。本文提出一种新方法GCNext，将物品特征直接整合到图表示中，通过图卷积网络构建富含特征的物品共现图，并以无监督方式学习物品嵌入。实验证明，在三个数据集上，将GCNext集成到序列推荐模型中可显著提升近邻方法及神经网络模型的性能。该灵活扩展易于接入主流方法，使MRR@20最高提升12.79%。
原文摘要 · Abstract (English)
In session-based recommender systems, predictions are based on the user's preceding behavior in the session. State-of-the-art sequential recommendation algorithms either use graph neural networks to model sessions in a graph or leverage the similarity of sessions by exploiting item features. In this paper, we combine these two approaches and propose a novel method, Graph Convolutional Network Extension (GCNext), which incorporates item features directly into the graph representation via graph convolutional networks. GCNext creates a feature-rich item co-occurrence graph and learns the corresponding item embeddings in an unsupervised manner. We show on three datasets that integrating GCNext into sequential recommendation algorithms significantly boosts the performance of nearest-neighbor methods as well as neural network models. Our flexible extension is easy to incorporate in state-of-the-art methods and increases the MRR@20 by up to 12.79%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。