论文标题
多功能增强协作的协作变量自动编码器用于标签建议
Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation
论文作者
论文摘要
向项目推荐适当的标签可以促进内容组织，检索，消费和其他应用程序，在这些应用程序中，Hybrid Tag推荐系统已被用于整合协作信息和内容信息以获得更好的建议。在本文中，我们提出了一个多功能增强的协作协作差异自动编码器（MA-CVAE），以供标记推荐，通过定义生成过程，将项目协作信息和项目协作信息和项目多功能信息，即内容和社交图。具体而言，该模型使用变异自动编码器（VAE）从不同项目的辅助信息中学习深层嵌入，该信息可以通过引入由深神经网络引入潜在变量参数来形成每个辅助信息的生成分布。此外，为了推荐新项目的标签，项目多功能潜在嵌入被用作替代物，通过项目解码器来预测每个标签的建议概率，其中在训练阶段中添加了重建损失，以通过不同的辅助嵌入来收缩反馈预测的反馈预测。此外，设计了一个归纳变分图自动编码器，可以在测试阶段推断出新项目节点，以便可以利用项目的社交嵌入到新项目中。对Movielens和Citeulike数据集进行的广泛实验证明了我们方法的有效性。
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.
