A comprehensive survey on trustworthy recommender systems
As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …
people make appropriate decisions in an effective and efficient way, by providing …
A survey of large language models
Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …
Recommendation as instruction following: A large language model empowered recommendation approach
In the past decades, recommender systems have attracted much attention in both research
and industry communities, and a large number of studies have been devoted to developing …
and industry communities, and a large number of studies have been devoted to developing …
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 …
Prompting large language models for recommender systems: A comprehensive framework and empirical analysis
Recently, large language models such as ChatGPT have showcased remarkable abilities in
solving general tasks, demonstrating the potential for applications in recommender systems …
solving general tasks, demonstrating the potential for applications in recommender systems …
Modeling two-way selection preference for person-job fit
Person-job fit is the core technique of online recruitment platforms, which can improve the
efficiency of recruitment by accurately matching the job positions with the job seekers …
efficiency of recruitment by accurately matching the job positions with the job seekers …
Uniform sequence better: Time interval aware data augmentation for sequential recommendation
Sequential recommendation is an important task to predict the next-item to access based on
a sequence of interacted items. Most existing works learn user preference as the transition …
a sequence of interacted items. Most existing works learn user preference as the transition …
Multimodal meta-learning for cold-start sequential recommendation
In this paper, we study the task of cold-start sequential recommendation, where new users
with very short interaction sequences come with time. We cast this problem as a few-shot …
with very short interaction sequences come with time. We cast this problem as a few-shot …
Horizontal Federated Recommender System: A Survey
L Wang, H Zhou, Y Bao, X Yan, G Shen… - ACM Computing …, 2024 - dl.acm.org
Due to underlying privacy-sensitive information in user-item interaction data, the risk of
privacy leakage exists in the centralized-training recommender system (RecSys). To this …
privacy leakage exists in the centralized-training recommender system (RecSys). To this …
Candidate-aware graph contrastive learning for recommendation
W He, G Sun, J Lu, XS Fang - Proceedings of the 46th International ACM …, 2023 - dl.acm.org
Recently, Graph Neural Networks (GNNs) have become a mainstream recommender system
method, where it captures high-order collaborative signals between nodes by performing …
method, where it captures high-order collaborative signals between nodes by performing …