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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 …
Recent developments in recommender systems: A survey
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 …
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
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 …
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
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 …
citation networks in academic websites, and threads data in online forums. Due to the …
Vip5: Towards multimodal foundation models for recommendation
Computer Vision (CV), Natural Language Processing (NLP), and Recommender Systems
(RecSys) are three prominent AI applications that have traditionally developed …
(RecSys) are three prominent AI applications that have traditionally developed …
A survey on trustworthy recommender systems
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 …
deployed in almost every corner of the web and facilitate the human decision-making …
Tutorial on large language models for recommendation
Foundation Models such as Large Language Models (LLMs) have significantly advanced
many research areas. In particular, LLMs offer significant advantages for recommender …
many research areas. In particular, LLMs offer significant advantages for recommender …
Bridging items and language: A transition paradigm for large language model-based recommendation
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 …
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
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 …
spaces and have become the de-facto standard for knowledge graph completion. Most …
Revisiting bundle recommendation for intent-aware product bundling
Product bundling represents a prevalent marketing strategy in both offline stores and e-
commerce systems. Despite its widespread use, previous studies on bundle …
commerce systems. Despite its widespread use, previous studies on bundle …