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User representation learning is vital to capture diverse user preferences,while it is also challenging as user intents are latent and scattered amongcomplex and different modalities of user-generated data, thus, not directlymeasurable. Inspired by the concept of user schema in social psychology, wetake a new perspective to perform user representation learning by constructinga shared latent space to capture the dependency among different modalities ofuser-generated data. Both users and topics are embedded to the same space toencode users' social connections and text content, to facilitate joint modelingof different modalities, via a probabilistic generative framework. We evaluatedthe proposed solution on large collections of Yelp reviews and StackOverflowdiscussion posts, with their associated network structures. The proposed modeloutperformed several state-of-the-art topic modeling based user models withbetter predictive power in unseen documents, and state-of-the-art networkembedding based user models with improved link prediction quality in unseennodes. The learnt user representations are also proved to be useful in contentrecommendation, e.g., expert finding in StackOverflow.

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