Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Recommendation

TL;DR

This paper introduces DeepCoNN, a deep neural network model that integrates user and item reviews to improve movie recommendations by capturing detailed preferences and attributes.

Contribution

The paper proposes a novel deep cooperative neural network architecture that simultaneously learns user behavior and item attributes from reviews, enhancing recommendation quality.

Findings

DeepCoNN outperforms baseline recommendation systems on multiple datasets.

The shared layer effectively models interactions between user preferences and item features.

Incorporating review data significantly reduces sparsity issues in recommendations.

Abstract

User evaluations include a significant quantity of information across online platforms. This information source has been neglected by the majority of existing recommendation systems, despite its potential to ease the sparsity issue and enhance the quality of suggestions. This work presents a deep model for concurrently learning item attributes and user behaviour from review text. Deep Cooperative Neural Network (DeepCoNN) is the suggested model consisting of two parallel neural networks connected in their final layers. One of the networks focuses on learning user behaviour from reviews submitted by the user, while the other network learns item attributes from user reviews. On top, a shared layer is added to connect these two networks. Similar to factorization machine approaches, the shared layer allows latent factors acquired for people and things to interact with each other. On a number of datasets, DeepCoNN surpasses all baseline recommendation systems, according to experimental findings.