Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders
This work addresses the limitation of traditional linear models in recommendation systems for handling high-dimensional data, though it appears incremental as it builds on existing neural collaborative filtering and autoencoder methods.
The paper tackles the problem of capturing complex interactions in context-aware recommendation systems by combining neural contextual matrix factorization with autoencoders to predict user ratings, and introduces a Conformal Prediction Rating (CPR) to enhance predictability, achieving competitive results on real-world datasets compared to state-of-the-art approaches.
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.