HCI.TOPHCI, made easy

arrow_backBack to questions View categoryarrow_forward

Recommendation, Personalization, and Exploration / Recommendation Algorithms, Ranking, and Social Recommendation

How can voice assistants enable proactive time management and improve user experience?

helpResearch questionRecommendation Algorithms, Ranking, and Social Recommendation

How can voice assistants enable proactive time management and improve user experience?

CUI '23Voicing Suggestions and Enabling Reflection: Results of an Expert Discussion on Proactive Assistants for Time Management

Similar questions

helpResearch questionRecommendation Algorithms, Ranking, and Social Recommendation

Compared with traditional ranked list interfaces, which more effectively adapts to changing user preferences: carousel interfaces?

IUI '25Under the Hood of Carousels: Investigating User Engagement and Navigation Effort in Multi-list Recommender Systems

helpResearch questionRecommendation Algorithms, Ranking, and Social Recommendation

Can carousel interfaces significantly reduce users' navigation costs?

IUI '25Under the Hood of Carousels: Investigating User Engagement and Navigation Effort in Multi-list Recommender Systems

helpResearch questionRecommendation Algorithms, Ranking, and Social Recommendation

Can carousel interfaces improve users' content exploration efficiency and decision comfort?

IUI '25Under the Hood of Carousels: Investigating User Engagement and Navigation Effort in Multi-list Recommender Systems

helpResearch questionRecommendation Algorithms, Ranking, and Social Recommendation

How can multi-objective recommendation ranking systems be designed and evaluated to optimize multiple goals while meeting the needs of different stakeholders?

IUI '25Orbit: A Framework for Designing and Evaluating Multi-objective Rankers

helpResearch questionRecommendation Algorithms, Ranking, and Social Recommendation

How can cross-functional team collaboration efficiency improve the design and evaluation of multi-objective rankers?

IUI '25Orbit: A Framework for Designing and Evaluating Multi-objective Rankers

helpResearch questionRecommendation Algorithms, Ranking, and Social Recommendation

Can comprehensive information analysis tools help users balance trade-offs among different objectives under design changes?

IUI '25Orbit: A Framework for Designing and Evaluating Multi-objective Rankers

Related papers

Under the Hood of Carousels: Investigating User Engagement and Navigation Effort in Multi-list Recommender Systems

Behnam Rahdari, Peter Brusilovsky

Orbit: A Framework for Designing and Evaluating Multi-objective Rankers

Chenyang Yang, Tesi Xiao, Michael Shavlovsky

Prefer2SD: A Human-in-the-Loop Approach to Balancing Similarity and Diversity in In-Game Friend Recommendations

Xiyuan Wang, Ziang Li, Sizhe Chen

Cinema Multiverse Lounge: Enhancing Film Appreciation via Multi-Agent Conversations

Kyusik Kim, Jeongwoo Ryu, Dongseok Heo

AI, Help Me Think—but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support

Leon Reicherts, Zelun Tony Zhang, Elisabeth von Oswald

Exploring the use of Speculative Concept Films for Co-Speculation around Data Ethics

Wyatt Olson, James Pierce

Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Content

Wenqi Li, Jui-Ching Kuo, Manyu Sheng

The Impact of Perceived Tone, Age, and Gender on Voice Assistant Persuasiveness in the Context of Product Recommendations

Sabid Bin Habib Pias, Ran Huang, Donald S. Williamson

Investigating the Potential of Group Recommendation Systems As a Medium of Social Interactions: A Case of Spotify Blend Experiences between Two Users

Daehyun Kwak, Soobin Park, Inha Cha

For Me or Not for Me? The Ease With Which Teens Navigate Accurate and Inaccurate Personalized Social Media Content

Nora McDonald, John S. Seberger, Afsaneh Razi

Mapping the Design Space of Teachable Social Media Feed Experiences

K. J. Kevin Feng, Xander Koo, Lawrence Tan

Cultural Differences in Friendship Network Behaviors: A Snapchat Case Study

Agrima Seth, Jiyin Cao, Xiaolin Shi