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Recommendation, Personalization, and Exploration / Media Content Recommendation Exploration and Control

Podcast recommendations are often limited to historical data and fail to meet users' diverse goals.

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

Similar questions

lightbulbPractical problemMedia Content Recommendation Exploration and Control

Users repeatedly express dislike for certain content, but the TikTok algorithm still recommends it.

CHI '25“They’ve Over-Emphasized That One Search”: Controlling Unwanted Content on TikTok's For You Page

lightbulbPractical problemMedia Content Recommendation Exploration and Control

Users may experience implicit psychological effects from recommendation algorithms but lack transparency and data support.

CHI '24Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations

lightbulbPractical problemMedia Content Recommendation Exploration and Control

Music recommendation platform algorithms often lack emotional resonance and user control, creating imbalanced experiences.

CHI '23Personalised Yet Impersonal: Listeners' Experiences Of Algorithmic Curation On Music Streaming Services

lightbulbPractical problemMedia Content Recommendation Exploration and Control

Users struggle to avoid distractions on YouTube, maintain focus, and achieve personal goals.

CHI '23SwitchTube: A Proof-of-Concept System Introducing "Adaptable Commitment Interfaces" as a Tool for Digital Wellbeing

lightbulbPractical problemMedia Content Recommendation Exploration and Control

Users struggle to break out of filter bubbles and discover more interests in music recommendation systems.

IUI '22TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems

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

Related papers

“They’ve Over-Emphasized That One Search”: Controlling Unwanted Content on TikTok's For You Page

Julie A. Vera, Sourojit Ghosh

Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations

Savvas Zannettou, Olivia Nemes-Nemeth, Oshrat Ayalon

Personalised Yet Impersonal: Listeners' Experiences Of Algorithmic Curation On Music Streaming Services

Sophie Freeman, Martin Gibbs, Bjorn Nansen

SwitchTube: A Proof-of-Concept System Introducing "Adaptable Commitment Interfaces" as a Tool for Digital Wellbeing

Kai Lukoff, Ulrik Lyngs, Karina Shirokova

TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems

Savvas Petridis, Nediyana Daskalova, Sarah Mennicken

OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers

Md Momen Bhuiyan, Carlos Augusto Bautista Isaza, Tanushree Mitra

Non-Linear Consumption of Videos Using a Sequence of Personalized Multimodal Fragments

Gaurav Verma, Trikay Nalamada, Keerti Harpavat

Interactive music genre exploration with visualization and mood control

Yu Liang, Martijn C. Willemsen

The Subconscious Director: Dynamically Personalizing Videos Using Gaze Data

Melanie Heck, Janick Edinger, Jonathan Bünemann

How the Design of YouTube Influences User Sense of Agency

Kai Lukoff, Ulrik Lyngs, Himanshu Zade