  Collaborative filtering (CF) is a core technique for recommender systems.
Traditional CF approaches exploit user-item relations (e.g., clicks, likes, and
views) only and hence they suffer from the data sparsity issue. Items are
usually associated with unstructured text such as article abstracts and product
reviews. We develop a Personalized Neural Embedding (PNE) framework to exploit
both interactions and words seamlessly. We learn such embeddings of users,
items, and words jointly, and predict user preferences on items based on these
learned representations. PNE estimates the probability that a user will like an
item by two terms---behavior factors and semantic factors. On two real-world
datasets, PNE shows better performance than four state-of-the-art baselines in
terms of three metrics. We also show that PNE learns meaningful word embeddings
by visualization.
