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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 indispensable and important component, providing personalized …
have become an indispensable and important component, providing personalized …
Self-supervised learning for recommender systems: A survey
In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …
tremendous success, but they still fall short of expectation when dealing with highly sparse …
Trustworthy llms: a survey and guideline for evaluating large language models' alignment
Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
Large language models as zero-shot conversational recommenders
In this paper, we present empirical studies on conversational recommendation tasks using
representative large language models in a zero-shot setting with three primary …
representative large language models in a zero-shot setting with three primary …
Representation learning with large language models for recommendation
Recommender systems have seen significant advancements with the influence of deep
learning and graph neural networks, particularly in capturing complex user-item …
learning and graph neural networks, particularly in capturing complex user-item …
A survey on the fairness of recommender systems
Recommender systems are an essential tool to relieve the information overload challenge
and play an important role in people's daily lives. Since recommendations involve …
and play an important role in people's daily lives. Since recommendations involve …
On generative agents in recommendation
Recommender systems are the cornerstone of today's information dissemination, yet a
disconnect between offline metrics and online performance greatly hinders their …
disconnect between offline metrics and online performance greatly hinders their …
XSimGCL: Towards extremely simple graph contrastive learning for recommendation
Contrastive learning (CL) has recently been demonstrated critical in improving
recommendation performance. The underlying principle of CL-based recommendation …
recommendation performance. The underlying principle of CL-based recommendation …
Are graph augmentations necessary? simple graph contrastive learning for recommendation
Contrastive learning (CL) recently has spurred a fruitful line of research in the field of
recommendation, since its ability to extract self-supervised signals from the raw data is well …
recommendation, since its ability to extract self-supervised signals from the raw data is well …
Black-box access is insufficient for rigorous ai audits
External audits of AI systems are increasingly recognized as a key mechanism for AI
governance. The effectiveness of an audit, however, depends on the degree of access …
governance. The effectiveness of an audit, however, depends on the degree of access …