Pre-train, Prompt, and Recommendation: A Comprehensive Survey of Language Modeling Paradigm Adaptations in Recommender Systems
The emergence of Pre-trained Language Models (PLMs) has achieved tremendous success
in the field of Natural Language Processing (NLP) by learning universal representations on …
in the field of Natural Language Processing (NLP) by learning universal representations on …
Sslrec: A self-supervised learning framework for recommendation
Self-supervised learning (SSL) has gained significant interest in recent years as a solution to
address the challenges posed by sparse and noisy data in recommender systems. Despite …
address the challenges posed by sparse and noisy data in recommender systems. Despite …
Protomf: Prototype-based matrix factorization for effective and explainable recommendations
Recent studies show the benefits of reformulating common machine learning models
through the concept of prototypes–representatives of the underlying data, used to calculate …
through the concept of prototypes–representatives of the underlying data, used to calculate …
SSLRec: A Self-Supervised Learning Library for Recommendation
Self-supervised learning (SSL) has gained significant interest in recent years as a solution to
address the challenges posed by sparse and noisy data in recommender systems. Despite …
address the challenges posed by sparse and noisy data in recommender systems. Despite …
A collaborative transfer learning framework for cross-domain recommendation
In the recommendation systems, there are multiple business domains to meet the diverse
interests and needs of users, and the click-through rate (CTR) of each domain can be quite …
interests and needs of users, and the click-through rate (CTR) of each domain can be quite …
Meta graph learning for long-tail recommendation
Highly skewed long-tail item distribution commonly hurts model performance on tail items in
recommendation systems, especially for graph-based recommendation models. We propose …
recommendation systems, especially for graph-based recommendation models. We propose …
Deep meta-learning in recommendation systems: A survey
Deep neural network based recommendation systems have achieved great success as
information filtering techniques in recent years. However, since model training from scratch …
information filtering techniques in recent years. However, since model training from scratch …
On size-oriented long-tailed graph classification of graph neural networks
The prevalence of graph structures attracts a surge of investigation on graph data, enabling
several downstream tasks such as multi-graph classification. However, in the multi-graph …
several downstream tasks such as multi-graph classification. However, in the multi-graph …
Intra-and inter-association attention network-enhanced policy learning for social group recommendation
Abstract Social Group Recommendation (SGR) is a critical task to recommend items to a
group of users in social network platforms, such as Meetup, Douban, Mofengwo, etc …
group of users in social network platforms, such as Meetup, Douban, Mofengwo, etc …
Memory bank augmented long-tail sequential recommendation
The goal of sequential recommendation is to predict the next item that a user would like to
interact with, by capturing her dynamic historical behaviors. However, most existing …
interact with, by capturing her dynamic historical behaviors. However, most existing …