Pre-train, prompt, and recommendation: A comprehensive survey of language modeling paradigm adaptations in recommender systems

P Liu, L Zhang, JA Gulla - Transactions of the Association for …, 2023‏ - direct.mit.edu
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 …

Recent developments in recommender systems: A survey

Y Li, K Liu, R Satapathy, S Wang… - IEEE Computational …, 2024‏ - ieeexplore.ieee.org
In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …

Large language models for generative recommendation: A survey and visionary discussions

L Li, Y Zhang, D Liu, L Chen - arxiv preprint arxiv:2309.01157, 2023‏ - arxiv.org
Large language models (LLM) not only have revolutionized the field of natural language
processing (NLP) but also have the potential to reshape many other fields, eg, recommender …

Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning

J Tan, S Geng, Z Fu, Y Ge, S Xu, Y Li… - Proceedings of the ACM …, 2022‏ - dl.acm.org
Structural data well exists in Web applications, such as social networks in social media,
citation networks in academic websites, and threads data in online forums. Due to the …

Vip5: Towards multimodal foundation models for recommendation

S Geng, J Tan, S Liu, Z Fu, Y Zhang - arxiv preprint arxiv:2305.14302, 2023‏ - arxiv.org
Computer Vision (CV), Natural Language Processing (NLP), and Recommender Systems
(RecSys) are three prominent AI applications that have traditionally developed …

A survey on trustworthy recommender systems

Y Ge, S Liu, Z Fu, J Tan, Z Li, S Xu, Y Li, Y **an… - ACM Transactions on …, 2024‏ - dl.acm.org
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Tutorial on large language models for recommendation

W Hua, L Li, S Xu, L Chen, Y Zhang - … of the 17th ACM Conference on …, 2023‏ - dl.acm.org
Foundation Models such as Large Language Models (LLMs) have significantly advanced
many research areas. In particular, LLMs offer significant advantages for recommender …

Bridging items and language: A transition paradigm for large language model-based recommendation

X Lin, W Wang, Y Li, F Feng, SK Ng… - Proceedings of the 30th …, 2024‏ - dl.acm.org
Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which
relies on two fundamental steps to bridge the recommendation item space and the language …

KRACL: Contrastive learning with graph context modeling for sparse knowledge graph completion

Z Tan, Z Chen, S Feng, Q Zhang, Q Zheng… - Proceedings of the …, 2023‏ - dl.acm.org
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional
spaces and have become the de-facto standard for knowledge graph completion. Most …

Revisiting bundle recommendation for intent-aware product bundling

Z Sun, K Feng, J Yang, H Fang, X Qu, YS Ong… - ACM Transactions on …, 2024‏ - dl.acm.org
Product bundling represents a prevalent marketing strategy in both offline stores and e-
commerce systems. Despite its widespread use, previous studies on bundle …