Recommending appropriate tags to items can facilitate content organization, retrieval, consumption and other applications, where hybrid tag recommender systems have been utilized to integrate collaborative information and content information for better recommendations. In this paper, we propose a multi-auxiliary augmented collaborative variational auto-encoder (MA-CVAE) for tag recommendation, which couples item collaborative information and item multi-auxiliary information, i.e., content and social graph, by defining a generative process. Specifically, the model learns deep latent embeddings from different item auxiliary information using variational auto-encoders (VAE), which could form a generative distribution over each auxiliary information by introducing a latent variable parameterized by deep neural network. Moreover, to recommend tags for new items, item multi-auxiliary latent embeddings are utilized as a surrogate through the item decoder for predicting recommendation probabilities of each tag, where reconstruction losses are added in the training phase to constrict the generation for feedback predictions via different auxiliary embeddings. In addition, an inductive variational graph auto-encoder is designed where new item nodes could be inferred in the test phase, such that item social embeddings could be exploited for new items. Extensive experiments on MovieLens and citeulike datasets demonstrate the effectiveness of our method.
翻译:推荐项目的适当标签可以促进内容组织、检索、消费和其他应用,在这些应用中,混合标签建议系统已经用于整合合作信息和内容信息,以便提出更好的建议。在本文件中,我们提议为标签建议建立一个多子增强合作性变异自动编码器(MA-CVAE),通过界定基因化过程,将项目的合作信息和项目多子信息,即内容和社会图,作为项目的合作信息和项目多子信息(即内容和社会图)作为代号,用来预测每个标签的建议概率。具体地说,模型利用变异自动编码器(VAE)从不同项目辅助信息中学习深潜嵌入信息,这可以通过引入深层神经网络的潜伏变量来对每一种辅助信息进行基因化传播。此外,为了推荐新项目标签的标签标签标签,通过项目解码器将多子潜在潜在嵌入用作一种代号,用以预测每个标签的建议概率,即内容和社会图,在培训阶段中增加损失,以限制生成通过不同辅助嵌入式嵌入的反馈预测。此外,还可以为每个辅助变异图形自动编码图,用于新的实验阶段,在新项目上进行新的磁性实验。