Recent advances of foundation language models-based continual learning: A survey
Recently, foundation language models (LMs) have marked significant achievements in the
domains of natural language processing and computer vision. Unlike traditional neural …
domains of natural language processing and computer vision. Unlike traditional neural …
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 …
Large language models are zero-shot rankers for recommender systems
Recently, large language models (LLMs)(eg, GPT-4) have demonstrated impressive general-
purpose task-solving abilities, including the potential to approach recommendation tasks …
purpose task-solving abilities, including the potential to approach recommendation tasks …
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. Existing recommendation models mainly learn the underlying …
and industry communities. Existing recommendation models mainly learn the underlying …
Chat-rec: Towards interactive and explainable llms-augmented recommender system
Large language models (LLMs) have demonstrated their significant potential to be applied
for addressing various application tasks. However, traditional recommender systems …
for addressing various application tasks. However, traditional recommender systems …
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
For a long time, different recommendation tasks require designing task-specific architectures
and training objectives. As a result, it is hard to transfer the knowledge and representations …
and training objectives. As a result, it is hard to transfer the knowledge and representations …
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 …
M6-rec: Generative pretrained language models are open-ended recommender systems
Industrial recommender systems have been growing increasingly complex, may
involve\emph {diverse domains} such as e-commerce products and user-generated …
involve\emph {diverse domains} such as e-commerce products and user-generated …
Online continual learning in image classification: An empirical survey
Online continual learning for image classification studies the problem of learning to classify
images from an online stream of data and tasks, where tasks may include new classes …
images from an online stream of data and tasks, where tasks may include new classes …
Where to go next for recommender systems? id-vs. modality-based recommender models revisited
Recommendation models that utilize unique identities (IDs for short) to represent distinct
users and items have been state-of-the-art (SOTA) and dominated the recommender …
users and items have been state-of-the-art (SOTA) and dominated the recommender …