JNET: Learning User Representations via Joint Network Embedding and Topic Embedding

1 Dec 2019Lin GongLu LinWeihao SongHongning Wang

User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable. Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data... (read more)

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