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. Item-s are usually associated with unstructured text such as article abstracts and product reviews. We develop a Personalized Neural Embedding (PNE) framework to exploit both interaction-s and words seamlessly. We learn such em-beddings of users, items, and words jointly, and predict user preferences on items based on these learned representations. PNF. estimates the probability that a user will like an item by two terms-behavior factors and semantic factors. On two real-world datasets, PNF. 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.
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