Translational Recommender Networks
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Representing relationships as translations in vector space lives at the heart of many neural embedding models such as word embeddings and knowledge graph embeddings. In this work, we study the connections of this translational principle with collaborative filtering algorithms. We propose Translational Recommender Networks (TransRec), a new attentive neural architecture that utilizes the translational principle to model the relationships between user and item pairs. Our model employs a neural attention mechanism over a Latent Relational Attentive Memory (LRAM) module to learn the latent relations between user-item pairs that best explains the interaction. By exploiting adaptive user-item specific translations in vector space, our model also alleviates the geometric inflexibility problem of other metric learning algorithms while enabling greater modeling capability and fine-grained fitting of users and items in vector space. The proposed architecture not only demonstrates the state-of-the-art performance across multiple recommendation benchmarks but also boasts of improved interpretability. Qualitative studies over the LRAM module shows evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of TransRec to uncover hidden relational structure within implicit datasets.
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