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Recommendation, Personalization, and Exploration / Conversational and Dialogue-Based Recommendation

Current recommendation systems struggle to dynamically adapt to changing user preferences and support future development goals.

lightbulbPractical problemConversational and Dialogue-Based Recommendation

Current recommendation systems struggle to dynamically adapt to changing user preferences and support future development goals.

CUI '24You Today, Better Tomorrow: Envisioning the Role of Conversation in Recommender Systems of the Future

Similar questions

lightbulbPractical problemConversational and Dialogue-Based Recommendation

Traditional recommender systems limit users' ability to express needs and cannot flexibly satisfy complex, vague requirements.

CHI '25User Experience of LLM-based Recommendation Systems: A Case of Music Recommendation

lightbulbPractical problemConversational and Dialogue-Based Recommendation

Smart speaker skills are numerous, but users struggle to find useful ones.

IUI '24Toward Faceted Skill Recommendation in Intelligent Personal Assistants

lightbulbPractical problemConversational and Dialogue-Based Recommendation

Users often feel frustrated due to interruptions or misunderstandings when conversing with recommender systems.

CUI '24Identifying Breakdowns in Conversational Recommender Systems using User Simulation

lightbulbPractical problemConversational and Dialogue-Based Recommendation

News assistants lack interactivity and personalization, resulting in poor user experience.

CUI '23Conversations with the News: Co-speculation into Conversational Interactions with News Content

lightbulbPractical problemConversational and Dialogue-Based Recommendation

User preferences expressed during shopping are typically conveyed through positive or negative language, which existing systems cannot fully leverage.

CUI '22Multimodal Conversational Fashion Recommendation with Positive and Negative Natural-Language Feedback

lightbulbPractical problemConversational and Dialogue-Based Recommendation

Users struggle to obtain high-quality personalized recommendations through natural conversation.

CUI '22Unifying Recommender Systems and Conversational User Interfaces

Related papers

User Experience of LLM-based Recommendation Systems: A Case of Music Recommendation

Sojeong Yun, Youn-kyung Lim

Toward Faceted Skill Recommendation in Intelligent Personal Assistants

Manveer Kalirai, Alex C Williams, Anastasia Kuzminykh

Identifying Breakdowns in Conversational Recommender Systems using User Simulation

Nolwenn Bernard, Krisztian Balog

Conversations with the News: Co-speculation into Conversational Interactions with News Content

Oda Elise Nordberg, Frode Guribye

Critiquing for Music Exploration in Conversational Recommender Systems

Wanling Cai, Yucheng Jin, Li Chen