[HTML][HTML] When large language models meet personalization: Perspectives of challenges and opportunities
The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …
intelligence. With the unprecedented scale of training and model parameters, the capability …
[HTML][HTML] Advances and challenges in conversational recommender systems: A survey
Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …
heavily used in a wide range of industry applications. However, static recommendation …
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 …
Conversational information seeking
Conversational information seeking (CIS) is concerned with a sequence of interactions
between one or more users and an information system. Interactions in CIS are primarily …
between one or more users and an information system. Interactions in CIS are primarily …
Personalized prompt learning for explainable recommendation
Providing user-understandable explanations to justify recommendations could help users
better understand the recommended items, increase the system's ease of use, and gain …
better understand the recommended items, increase the system's ease of use, and gain …
A survey on conversational recommender systems
Recommender systems are software applications that help users to find items of interest in
situations of information overload. Current research often assumes a one-shot interaction …
situations of information overload. Current research often assumes a one-shot interaction …
Towards unified conversational recommender systems via knowledge-enhanced prompt learning
Conversational recommender systems (CRS) aim to proactively elicit user preference and
recommend high-quality items through natural language conversations. Typically, a CRS …
recommend high-quality items through natural language conversations. Typically, a CRS …
Interactive path reasoning on graph for conversational recommendation
Traditional recommendation systems estimate user preference on items from past interaction
history, thus suffering from the limitations of obtaining fine-grained and dynamic user …
history, thus suffering from the limitations of obtaining fine-grained and dynamic user …
Leveraging large language models in conversational recommender systems
L Friedman, S Ahuja, D Allen, Z Tan… - arXiv preprint arXiv …, 2023 - arxiv.org
A Conversational Recommender System (CRS) offers increased transparency and control to
users by enabling them to engage with the system through a real-time multi-turn dialogue …
users by enabling them to engage with the system through a real-time multi-turn dialogue …
Rethinking the evaluation for conversational recommendation in the era of large language models
The recent success of large language models (LLMs) has shown great potential to develop
more powerful conversational recommender systems (CRSs), which rely on natural …
more powerful conversational recommender systems (CRSs), which rely on natural …