Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders

TL;DR

This paper proposes a novel framework combining neural contextual matrix factorization with autoencoders to improve recommendation accuracy and introduces a conformal prediction rating method to quantify uncertainty in predictions.

Contribution

It introduces an innovative framework integrating autoencoders with neural collaborative filtering and extends conformal prediction to rating prediction tasks.

Findings

Enhanced recommendation accuracy demonstrated on real-world datasets.

The conformal prediction rating provides reliable uncertainty quantification.

Framework outperforms state-of-the-art methods in experiments.

Abstract

In the field of Recommender Systems (RS), neural collaborative filtering represents a significant milestone by combining matrix factorization and deep neural networks to achieve promising results. Traditional methods like matrix factorization often rely on linear models, limiting their capability to capture complex interactions between users, items, and contexts. This limitation becomes particularly evident with high-dimensional datasets due to their inability to capture relationships among users, items, and contextual factors. Unsupervised learning and dimension reduction tasks utilize autoencoders, neural network-based models renowned for their capacity to encode and decode data. Autoencoders learn latent representations of inputs, reducing dataset size while capturing complex patterns and features. In this paper, we introduce a framework that combines neural contextual matrix factorization with autoencoders to predict user ratings for items. We provide a comprehensive overview of the framework's design and implementation. To evaluate its performance, we conduct experiments on various real-world datasets and compare the results against state-of-the-art approaches. We also extend the concept of conformal prediction to prediction rating and introduce a Conformal Prediction Rating (CPR). For RS, we define the nonconformity score, a key concept of conformal prediction, and demonstrate that it satisfies the exchangeability property.