[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 …
Proactive conversational agents in the post-chatgpt world
ChatGPT and similar large language model (LLM) based conversational agents have
brought shock waves to the research world. Although astonished by their human-like …
brought shock waves to the research world. Although astonished by their human-like …
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 …
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 …
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 …
KuaiRec: A fully-observed dataset and insights for evaluating recommender systems
The progress of recommender systems is hampered mainly by evaluation as it requires real-
time interactions between humans and systems, which is too laborious and expensive. This …
time interactions between humans and systems, which is too laborious and expensive. This …
Unified conversational recommendation policy learning via graph-based reinforcement learning
Conversational recommender systems (CRS) enable the traditional recommender systems
to explicitly acquire user preferences towards items and attributes through interactive …
to explicitly acquire user preferences towards items and attributes through interactive …
A unified multi-task learning framework for multi-goal conversational recommender systems
Recent years witnessed several advances in developing multi-goal conversational
recommender systems (MG-CRS) that can proactively attract users' interests and naturally …
recommender systems (MG-CRS) that can proactively attract users' interests and naturally …
C²-crs: Coarse-to-fine contrastive learning for conversational recommender system
Conversational recommender systems (CRS) aim to recommend suitable items to users
through natural language conversations. For developing effective CRSs, a major technical …
through natural language conversations. For developing effective CRSs, a major technical …
How can recommender systems benefit from large language models: A survey
With the rapid development of online services, recommender systems (RS) have become
increasingly indispensable for mitigating information overload. Despite remarkable …
increasingly indispensable for mitigating information overload. Despite remarkable …