Personalized Neural Embeddings for Collaborative Filtering with Text
This addresses the problem of improving recommendation accuracy for users in systems with sparse interaction data, representing an incremental advancement by combining existing interaction and text data in a novel neural framework.
The paper tackles the data sparsity issue in collaborative filtering by developing a Personalized Neural Embedding (PNE) framework that integrates user-item interactions and unstructured text, resulting in better performance than four state-of-the-art baselines on two real-world datasets across three metrics.
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.