Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects
Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …
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 …
A survey on large language models: Applications, challenges, limitations, and practical usage
Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …
Large language models are competitive near cold-start recommenders for language-and item-based preferences
Traditional recommender systems leverage users' item preference history to recommend
novel content that users may like. However, modern dialog interfaces that allow users to …
novel content that users may like. However, modern dialog interfaces that allow users to …
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 …
A review of modern recommender systems using generative models (gen-recsys)
Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …
source. However, deep generative models now have the capability to model and sample …
Enhancing recommendation diversity by re-ranking with large language models
Recommender Systems (RS) should provide diverse recommendations, not just relevant
ones. Diversity helps handle uncertainty and offers users meaningful choices. The literature …
ones. Diversity helps handle uncertainty and offers users meaningful choices. The literature …
Towards understanding and mitigating unintended biases in language model-driven conversational recommendation
Abstract Conversational Recommendation Systems (CRSs) have recently started to
leverage pretrained language models (LM) such as BERT for their ability to semantically …
leverage pretrained language models (LM) such as BERT for their ability to semantically …
User perception of recommendation explanation: Are your explanations what users need?
As recommender systems become increasingly important in daily human decision-making,
users are demanding convincing explanations to understand why they get the specific …
users are demanding convincing explanations to understand why they get the specific …
Factual and informative review generation for explainable recommendation
Recent models can generate fluent and grammatical synthetic reviews while accurately
predicting user ratings. The generated reviews, expressing users' estimated opinions …
predicting user ratings. The generated reviews, expressing users' estimated opinions …