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A comprehensive survey on deep graph representation learning
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 …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Graph neural networks in recommender systems: a survey
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 …
alleviate such information overload. Due to the important application value of recommender …
Text is all you need: Learning language representations for sequential recommendation
Sequential recommendation aims to model dynamic user behavior from historical
interactions. Existing methods rely on either explicit item IDs or general textual features for …
interactions. Existing methods rely on either explicit item IDs or general textual features for …
Recommender systems with generative retrieval
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 …
candidates in the same unified space, followed by approximate nearest neighbor search to …
Towards universal sequence representation learning for recommender systems
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 …
learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL …
Filter-enhanced MLP is all you need for sequential recommendation
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 …
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
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 …
Adapting large language models by integrating collaborative semantics for recommendation
Recently, large language models (LLMs) have shown great potential in recommender
systems, either improving existing recommendation models or serving as the backbone …
systems, either improving existing recommendation models or serving as the backbone …
Learning vector-quantized item representation for transferable sequential recommenders
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 …
recommender systems. The basic idea is to employ pre-trained language models (PLM) to …
Self-supervised hypergraph convolutional networks for session-based recommendation
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 …
point. As user profiles are generally not available in this scenario, capturing the user intent …