# Personalized Neural Embeddings for Collaborative Filtering with Text

## TL;DR

This paper introduces PNE, a neural embedding framework that combines user-item interactions and item text to improve recommendation accuracy, addressing data sparsity in collaborative filtering.

## Contribution

The paper presents a novel joint embedding approach that integrates textual information with interaction data for personalized recommendations.

## Key findings

- PNE outperforms four state-of-the-art baselines on real-world datasets.
- PNE effectively learns meaningful word embeddings.
- The model improves recommendation accuracy by leveraging text and interaction data.

## 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.