Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an important component of our daily life, providing personalized suggestions …
have become an important component of our daily life, providing personalized suggestions …
Evaluating recommender systems: survey and framework
E Zangerle, C Bauer - ACM Computing Surveys, 2022 - dl.acm.org
The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …
endeavor: many facets need to be considered in configuring an adequate and effective …
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
For a long time, different recommendation tasks require designing task-specific architectures
and training objectives. As a result, it is hard to transfer the knowledge and representations …
and training objectives. As a result, it is hard to transfer the knowledge and representations …
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 …
A survey on knowledge graph-based recommender systems
To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …
applications, recommender systems have been developed to model users' preferences …
[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 …
EDMF: Efficient deep matrix factorization with review feature learning for industrial recommender system
Recommendation accuracy is a fundamental problem in the quality of the recommendation
system. In this article, we propose an efficient deep matrix factorization (EDMF) with review …
system. In this article, we propose an efficient deep matrix factorization (EDMF) with review …
Reinforcement knowledge graph reasoning for explainable recommendation
Recent advances in personalized recommendation have sparked great interest in the
exploitation of rich structured information provided by knowledge graphs. Unlike most …
exploitation of rich structured information provided by knowledge graphs. Unlike most …
Personalized top-n sequential recommendation via convolutional sequence embedding
Top-N sequential recommendation models each user as a sequence of items interacted in
the past and aims to predict top-N ranked items that a user will likely interact in a» near …
the past and aims to predict top-N ranked items that a user will likely interact in a» near …
A review-aware graph contrastive learning framework for recommendation
Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …
and item embedding learning, followed by the user-item interaction modeling. By utilizing …