Personalized Neural Embeddings for Collaborative Filtering with Text

Abstract

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.

arXiv commentNAACL 2019 short papers, oral presentation

References in corpus (7)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Collaborative Memory Network for Recommendation Systems
• TransNets: Learning to Transform for Recommendation
• Ask the GRU: Multi-Task Learning for Deep Text Recommendations
• Wide & Deep Learning for Recommender Systems
• Neural Network Matrix Factorization
• Integrating Reviews into Personalized Ranking for Cold Start Recommendation