helpResearch questionMedia Content Recommendation Exploration and Control
What strategies do users adopt when facing TikTok recommendations that do not match their interests?Direction: Recommendation, Personalization, and Exploration
Media Content Recommendation Exploration and Control
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48 items
helpResearch questionMedia Content Recommendation Exploration and Control
Why do TikTok recommendation algorithms show 'algorithmic persistence' in response to users' rejection signals?helpResearch questionMedia Content Recommendation Exploration and Control
Can users' behavioral strategies effectively improve recommender adaptivity?lightbulbPractical problemMedia Content Recommendation Exploration and Control
Users repeatedly express dislike for certain content, but the TikTok algorithm still recommends it.helpResearch questionMedia Content Recommendation Exploration and Control
How do TikTok's short-video recommendation algorithms affect user behavior and engagement?helpResearch questionMedia Content Recommendation Exploration and Control
How do users' usage time and engagement patterns change over time?helpResearch questionMedia Content Recommendation Exploration and Control
Why do videos from unfollowed accounts perform better in user interaction?lightbulbPractical problemMedia Content Recommendation Exploration and Control
Users may experience implicit psychological effects from recommendation algorithms but lack transparency and data support.helpResearch questionMedia Content Recommendation Exploration and Control
How can interactive recommendation systems enable goal-directed podcast exploration?IUI '23Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender Systems
helpResearch questionMedia Content Recommendation Exploration and Control
How do goal-setting interfaces affect users' content exploration behavior and cognitive load?IUI '23Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender Systems
helpResearch questionMedia Content Recommendation Exploration and Control
Can interactive tools help users break filter bubbles in recommendation systems and achieve diverse content consumption?IUI '23Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender Systems
lightbulbPractical problemMedia Content Recommendation Exploration and Control
Podcast recommendations are often limited to historical data and fail to meet users' diverse goals.IUI '23Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender Systems
helpResearch questionMedia Content Recommendation Exploration and Control
How can music recommendation systems balance personalization with users' needs for transparency and control?helpResearch questionMedia Content Recommendation Exploration and Control
How does the emotional atmosphere of recommended content affect user satisfaction and experience?helpResearch questionMedia Content Recommendation Exploration and Control
How do users' needs and preferences differ across listening modes?lightbulbPractical problemMedia Content Recommendation Exploration and Control
Music recommendation platform algorithms often lack emotional resonance and user control, creating imbalanced experiences.helpResearch questionMedia Content Recommendation Exploration and Control
When facing YouTube recommendation systems and autoplay features, how can interfaces be designed to better balance users' short-term interests and long-term goals?helpResearch questionMedia Content Recommendation Exploration and Control
How do adaptable commitment interfaces affect users' sense of autonomy, satisfaction, and goal alignment?helpResearch questionMedia Content Recommendation Exploration and Control
What are users' switching behaviors and scenarios when using different modes (exploration mode and focus mode)?lightbulbPractical problemMedia Content Recommendation Exploration and Control
Users struggle to avoid distractions on YouTube, maintain focus, and achieve personal goals.helpResearch questionMedia Content Recommendation Exploration and Control
How can personalized data help users deeply explore and better understand their preferred music styles?helpResearch questionMedia Content Recommendation Exploration and Control
If linear constraints in recommendation systems are removed, what exploration strategies will users adopt, and how can these strategies be better supported?helpResearch questionMedia Content Recommendation Exploration and Control
How does learning about one's music preferences help users more effectively express interests in recommendation environments?lightbulbPractical problemMedia Content Recommendation Exploration and Control
Users struggle to break out of filter bubbles and discover more interests in music recommendation systems.helpResearch questionMedia Content Recommendation Exploration and Control
Can real-time contextual data from smart wristbands effectively improve personalized music recommendation accuracy?UbiComp '22Towards Ubiquitous Personalized Music Recommendation with Smart Bracelets
helpResearch questionMedia Content Recommendation Exploration and Control
How does the multitask learning model (MUMR) optimize recommendation performance by combining emotion prediction and music preference prediction?UbiComp '22Towards Ubiquitous Personalized Music Recommendation with Smart Bracelets
helpResearch questionMedia Content Recommendation Exploration and Control
How do contextual factors such as activity, environment, and emotion influence music preferences?UbiComp '22Towards Ubiquitous Personalized Music Recommendation with Smart Bracelets
lightbulbPractical problemMedia Content Recommendation Exploration and Control
Existing music recommendation systems fail to adequately account for users' real-time context and emotional state.UbiComp '22Towards Ubiquitous Personalized Music Recommendation with Smart Bracelets
helpResearch questionMedia Content Recommendation Exploration and Control
Can exchanging YouTube recommendations help users break algorithmic filter bubbles and discover diverse viewpoints?helpResearch questionMedia Content Recommendation Exploration and Control
By sharing and browsing recommendations with strangers, can users expand interests or reflect on their content preferences?helpResearch questionMedia Content Recommendation Exploration and Control
How do social comparison mechanisms of recommended content affect users' cross-cultural understanding and cognitive boundaries?lightbulbPractical problemMedia Content Recommendation Exploration and Control
Users struggle to escape recommendation algorithm filter bubbles and access diverse content and viewpoints.helpResearch questionMedia Content Recommendation Exploration and Control
How can personalized multimodal video segments be generated to support nonlinear video consumption?helpResearch questionMedia Content Recommendation Exploration and Control
Can combining multimodal understanding (e.g., images and text) improve narrative coherence and alignment with user preferences in video segments?helpResearch questionMedia Content Recommendation Exploration and Control
How can an information-optimization scoring mechanism balance diversity, coverage, user preference alignment, and narrative coherence in video segments?lightbulbPractical problemMedia Content Recommendation Exploration and Control
Users struggle to quickly understand long video content or locate parts of interest.helpResearch questionMedia Content Recommendation Exploration and Control
Which visualization approach better helps users understand recommendation content and music genre relationships in recommender systems?helpResearch questionMedia Content Recommendation Exploration and Control
How do emotion control sliders affect users' exploration behavior in music recommender systems?helpResearch questionMedia Content Recommendation Exploration and Control
Can combining two visualization techniques with emotion control improve transparency and interactivity around recommended content?lightbulbPractical problemMedia Content Recommendation Exploration and Control
Users exploring unfamiliar music genres struggle to understand the personal relevance of recommended content.helpResearch questionMedia Content Recommendation Exploration and Control
How can gaze data be used to dynamically personalize video content in real time?helpResearch questionMedia Content Recommendation Exploration and Control
How does dynamically personalized video content affect viewers' focus and engagement?helpResearch questionMedia Content Recommendation Exploration and Control
How do machine learning methods compare to simple majority voting in predicting personalized video preferences?lightbulbPractical problemMedia Content Recommendation Exploration and Control
Viewers struggle to balance effortless entertainment with personalized content choices.helpResearch questionMedia Content Recommendation Exploration and Control
How do YouTube's recommendation algorithms and autoplay features affect users' sense of autonomy?helpResearch questionMedia Content Recommendation Exploration and Control
Which internal design mechanisms can enhance users' sense of autonomy on YouTube?helpResearch questionMedia Content Recommendation Exploration and Control
How can adjusting interface control options meet different users' needs?lightbulbPractical problemMedia Content Recommendation Exploration and Control
Users feel YouTube's design makes it difficult to control their own usage time.Related papers
CHI 2025
“They’ve Over-Emphasized That One Search”: Controlling Unwanted Content on TikTok's For You Page
Julie A. Vera, Sourojit Ghosh
CHI 2024
Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations
Savvas Zannettou, Olivia Nemes-Nemeth, Oshrat Ayalon
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Personalised Yet Impersonal: Listeners' Experiences Of Algorithmic Curation On Music Streaming Services
Sophie Freeman, Martin Gibbs, Bjorn Nansen
CHI 2023
SwitchTube: A Proof-of-Concept System Introducing "Adaptable Commitment Interfaces" as a Tool for Digital Wellbeing
Kai Lukoff, Ulrik Lyngs, Karina Shirokova
IUI 2022
TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems
Savvas Petridis, Nediyana Daskalova, Sarah Mennicken
CHI 2022
OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers
Md Momen Bhuiyan, Carlos Augusto Bautista Isaza, Tanushree Mitra
IUI 2021
Non-Linear Consumption of Videos Using a Sequence of Personalized Multimodal Fragments
Gaurav Verma, Trikay Nalamada, Keerti Harpavat
IUI 2021
Interactive music genre exploration with visualization and mood control
Yu Liang, Martijn C. Willemsen
IUI 2021
The Subconscious Director: Dynamically Personalizing Videos Using Gaze Data
Melanie Heck, Janick Edinger, Jonathan Bünemann
CHI 2021
How the Design of YouTube Influences User Sense of Agency
Kai Lukoff, Ulrik Lyngs, Himanshu Zade
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