基于深度学习的推荐系统:模型、数据集、评估指标和未来趋势概述

Kyle Ong, S. Haw, K. Ng
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引用次数: 7

摘要

近年来数据的增长推动了深度学习在许多计算机科学相关领域的出现,包括推荐系统(RS)。深度学习已经成为解决方案;克服传统推荐模型的障碍。深度学习能够通过学习非线性和非平凡的用户-物品关系,提取用户和物品的深度和抽象特征表示来提高推荐质量。然而,RS中的深度学习仍然是一个新兴的、蓬勃发展的领域。本文的贡献是双重的。首先,我们将提供一些关于RS进展的见解,重点是深度学习模型、数据集和评估指标。其次,我们对基于深度学习的RS领域的当前趋势进行了扩展,并提出了几个可能的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Based-Recommendation System: An Overview on Models, Datasets, Evaluation Metrics, and Future Trends
The growth of data in recent years has motivated the emergence of deep learning in many Computer Sciences related fields including Recommender System (RS). Deep learning has emerged as the solution; overcoming the obstacles of traditional recommendation models. Deep learning is able to enhance recommendation quality by learning non-linear and non-trivial user-item relationship, and extracting deep and abstract feature representations for users and items. However, deep learning in RS is still new and flourishing. The contribution of this paper is two-folds. Firstly, we will be providing several insights on the advances of RS focusing on deep-learning models, datasets and evaluation metrics. Secondly, we expand on the current trend and provide several possible research directions in the field of deep learning-based RS.
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