Deep Learning-Based Context-Aware Recommender System Considering Contextual Features

A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users. However, as a user preferences matrix is expanded in a multidimensional matrix, data sparsity is maximized. In this paper, we propose a deep learning-based context-aware recommender system that considers the contextual features. Based on existing deep learning models, we combine a neural network and autoencoder to extract characteristics and predict scores in the process of restoring input data. The newly proposed model is able to easily reflect various type of contextual information and predicts user preferences by considering the feature of user, item and context. The experimental results confirm that the proposed method is mostly superior to the existing method in all datasets. Also, for the dataset with data sparsity problem, it was confirmed that the performance of the proposed method is higher than that of existing methods. The proposed method has higher precision by 0.01-0.05 than other recommender systems in a dataset with many context dimensions. And it showed good performance with a high precision of 0.03 to 0.09 in a small dimensional dataset.

Automated summary

This paper proposes a deep learning-based context-aware recommender system that effectively addresses data sparsity and demonstrates superior performance across various datasets.