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.

本论文提出了一种个性化神经嵌入框架 (PNE)，通过融合互动和单词来预测用户对物品的期望，解决了传统协同过滤方法中物品与用户关系稀疏的问题，并在两个真实数据集上展示了比四个基线模型更好的性能表现。同时，通过可视化展示了 PNE 可以学习到有意义的单词嵌入。