A comprehensive survey on deep graph representation learning

W Ju, Z Fang, Y Gu, Z Liu, Q Long, Z Qiao, Y Qin… - Neural Networks, 2024 - Elsevier
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Graph neural networks in recommender systems: a survey

S Wu, F Sun, W Zhang, X **e, B Cui - ACM Computing Surveys, 2022 - dl.acm.org
With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Text is all you need: Learning language representations for sequential recommendation

J Li, M Wang, J Li, J Fu, X Shen, J Shang… - Proceedings of the 29th …, 2023 - dl.acm.org
Sequential recommendation aims to model dynamic user behavior from historical
interactions. Existing methods rely on either explicit item IDs or general textual features for …

Recommender systems with generative retrieval

S Rajput, N Mehta, A Singh… - Advances in …, 2023 - proceedings.neurips.cc
Modern recommender systems perform large-scale retrieval by embedding queries and item
candidates in the same unified space, followed by approximate nearest neighbor search to …

Towards universal sequence representation learning for recommender systems

Y Hou, S Mu, WX Zhao, Y Li, B Ding… - Proceedings of the 28th …, 2022 - dl.acm.org
In order to develop effective sequential recommenders, a series of sequence representation
learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL …

Filter-enhanced MLP is all you need for sequential recommendation

K Zhou, H Yu, WX Zhao, JR Wen - … of the ACM web conference 2022, 2022 - dl.acm.org
Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …

A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation

L Wu, X He, X Wang, K Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Adapting large language models by integrating collaborative semantics for recommendation

B Zheng, Y Hou, H Lu, Y Chen, WX Zhao… - 2024 IEEE 40th …, 2024 - ieeexplore.ieee.org
Recently, large language models (LLMs) have shown great potential in recommender
systems, either improving existing recommendation models or serving as the backbone …

Learning vector-quantized item representation for transferable sequential recommenders

Y Hou, Z He, J McAuley, WX Zhao - … of the ACM Web Conference 2023, 2023 - dl.acm.org
Recently, the generality of natural language text has been leveraged to develop transferable
recommender systems. The basic idea is to employ pre-trained language models (PLM) to …

Self-supervised hypergraph convolutional networks for session-based recommendation

X **a, H Yin, J Yu, Q Wang, L Cui… - Proceedings of the AAAI …, 2021 - ojs.aaai.org
Session-based recommendation (SBR) focuses on next-item prediction at a certain time
point. As user profiles are generally not available in this scenario, capturing the user intent …