主题增强的多层次图神经网络会话推荐模型

Topic-Enhanced Multi-level Graph Neural Network for Session-Based Recommendation

基于会话的推荐旨在基于会话内数据,为匿名或未登录用户做出推荐.现有的研究工作通常仅以会话中单个商品作为最小单位进行建模,忽略商品在不同感受野下的表征.同时,尚未挖掘会话序列中蕴含的商品隐式主题信息.为了缓解上述问题,文中提出主题增强的多层次图神经网络会话推荐模型(Topic-Enhanced Multi-level Graph Neural Network for Session-Based Recommendation,TEMGNN).首先,设计多层次商品嵌入学习模块,拓宽商品的感受野,获取不同粒度下的商品表示.然后,结合文中提出的多层次图神经网络进行同粒度和跨粒度下的商品信息传播,捕获更丰富的商品嵌入表征.此外,提出商品主题学习模块,在不依赖任何商品属性信息的前提下,抽取商品在隐空间下的主题共性,并以显式的向量空间投影方式自动形成商品的主题表示,用于增强模型推荐性能.在3个基准数据集上的实验表明,TEMGNN的表现较优.

Session-based recommendation(SBR)aims to provide recommendations for anonymous users or users who are not logged in based on data in the session.The existing research models a single item in the session as the smallest unit,ignoring the item representation in different receptive fields.Moreover,the implicit topic information contained in the session sequence is not mined.To alleviate these issues,a topic-enhanced multi-level graph neural network(TEMGNN)for SBR is proposed.Firstly,a multi-level item embedding learning module is designed to broaden the receptive fields of item and obtain the representation of items at different granularities.Then,the proposed multi-level graph neural network is employed to propagate the item information with and cross granularities,capturing richer item embedding representation.Furthermore,a topic learning module is proposed to extract the topic commonalities of items in hidden space and automatically form topic representations of items by explicit vector space projection without relying on any item attribute information.Thus,the recommendation performance of the model is enhanced.Experiments on three benchmark datasets show the superiority of TEMGNN.