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

Everyday location-based recommendation and surveillance technologies infringe on user privacy and autonomy.

lightbulbPractical problemRecommendation Algorithms, Ranking, and Social Recommendation

Everyday location-based recommendation and surveillance technologies infringe on user privacy and autonomy.

CHI '25Exploring the use of Speculative Concept Films for Co-Speculation around Data Ethics

Similar questions

lightbulbPractical problemRecommendation Algorithms, Ranking, and Social Recommendation

Users struggle to efficiently explore and discover content in content-rich recommendation systems.

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

lightbulbPractical problemRecommendation Algorithms, Ranking, and Social Recommendation

Multi-objective optimization design in recommender systems is complex, and team collaboration efficiency is low.

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

lightbulbPractical problemRecommendation Algorithms, Ranking, and Social Recommendation

Players in multiplayer online games struggle to find social recommendations that balance similarity and diversity.

IUI '25Prefer2SD: A Human-in-the-Loop Approach to Balancing Similarity and Diversity in In-Game Friend Recommendations

lightbulbPractical problemRecommendation Algorithms, Ranking, and Social Recommendation

Film fans lack deep, multi-perspective support when discussing movies online.

CHI '25Cinema Multiverse Lounge: Enhancing Film Appreciation via Multi-Agent Conversations

lightbulbPractical problemRecommendation Algorithms, Ranking, and Social Recommendation

When handling complex problems, users struggle to integrate AI suggestions into decisions and may become overly dependent.

CHI '25AI, Help Me Think—but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support

lightbulbPractical problemRecommendation Algorithms, Ranking, and Social Recommendation

Personalized recommendations lack diversity, potentially limiting UX.

CHI '25Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Content

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