A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation
Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …
understanding, research in recommendation has shifted to inventing new recommender …
Deep learning based recommender system: A survey and new perspectives
With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …
effective strategy to overcome information overload. The utility of recommender systems …
Diffusion recommender model
Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-
Encoders (VAEs) are widely utilized to model the generative process of user interactions …
Encoders (VAEs) are widely utilized to model the generative process of user interactions …
[PDF][PDF] 基于深度学习的推荐系统研究综述
黄立威, 江碧涛, 吕守业, 刘艳博, 李德毅 - 计算机学报, 2018 - cdn.jsdelivr.net
摘要深度学习是机器学习领域一个重要研究方向, 近年来在图像处理, 自然语言理解,
语音识别和在线广告等领域取得了突破性进展. 将深度学习融入推荐系统中 …
语音识别和在线广告等领域取得了突破性进展. 将深度学习融入推荐系统中 …
Neural graph collaborative filtering
Learning vector representations (aka. embeddings) of users and items lies at the core of
modern recommender systems. Ranging from early matrix factorization to recently emerged …
modern recommender systems. Ranging from early matrix factorization to recently emerged …
Are we really making much progress? A worrying analysis of recent neural recommendation approaches
M Ferrari Dacrema, P Cremonesi… - Proceedings of the 13th …, 2019 - dl.acm.org
Deep learning techniques have become the method of choice for researchers working on
algorithmic aspects of recommender systems. With the strongly increased interest in …
algorithmic aspects of recommender systems. With the strongly increased interest in …
Graph-refined convolutional network for multimedia recommendation with implicit feedback
Reorganizing implicit feedback of users as a user-item interaction graph facilitates the
applications of graph convolutional networks (GCNs) in recommendation tasks. In the …
applications of graph convolutional networks (GCNs) in recommendation tasks. In the …
Melu: Meta-learned user preference estimator for cold-start recommendation
This paper proposes a recommender system to alleviate the cold-start problem that can
estimate user preferences based on only a small number of items. To identify a user's …
estimate user preferences based on only a small number of items. To identify a user's …
Learning disentangled representations for recommendation
User behavior data in recommender systems are driven by the complex interactions of many
latent factors behind the users' decision making processes. The factors are highly entangled …
latent factors behind the users' decision making processes. The factors are highly entangled …
A review on deep learning for recommender systems: challenges and remedies
Recommender systems are effective tools of information filtering that are prevalent due to
increasing access to the Internet, personalization trends, and changing habits of computer …
increasing access to the Internet, personalization trends, and changing habits of computer …