Jennifer Thom

schoolSpotifybar_chart#1894 Global Rank

Total Papers

HCI Rank

#1894

Research Areas

Years Active

Recent Publications

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

Recommender systems are ubiquitous and influence the information we consume on a daily basis by helping us navigate vast catalogs of information like music databases. However, their linear approach of surfacing content in ranked lists limits their ability to help us grow and understand our personal preferences. In thi…

CSCW '21CorrespondingVoice and Speech

Let Me Ask You This: How Can a Voice Assistant Elicit Explicit User Feedback?

Voice assistants offer users access to an increasing variety of personalized functionalities. The researchers and engineers who build these experiences rely on various signals from users to create the machine learning models powering them. One type of signal is explicit in situ feedback. While collecting explicit in s…

CHI '214th AuthorAI Ethics, Fairness & Accountability +1

Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits

In order to support fairness-forward thinking by machine learning (ML) practitioners, fairness researchers have created toolkits that aim to transform state-of-the-art research contributions into easily-accessible APIs. Despite these efforts, recent research indicates a disconnect between the needs of practitioners an…

DIS '203rd AuthorVoice User Interface (VUI) Design +2

Using Remote Controlled Speech Agents to Explore Music Experience in Context

It can be difficult for user researchers to explore how people might interact with interactive systems in everyday contexts; time and space limitations make it hard to be present everywhere that technology is used. Digital music services are one domain where designing for context is important given the myriad places p…

CHI '20CorrespondingIntelligent Voice Assistants (Alexa, Siri, etc.) +1

Giving Voice to Silent Data: Designing with Personal Music Listening History

Music streaming services collect listener data to support personalization and discovery of their extensive catalogs. Yet this data is typically used in ways that are not immediately apparent to listeners. We conducted design workshops with ten Spotify listeners to imagine future voice assistant (VA) interactions lever…

CHI '19CorrespondingRecommender System UX

Just Give Me What I Want: How People Use and Evaluate Music Search

Music-streaming platforms offer users a large amount of content for consumption. Finding the right music can be challenging and users often need to search through extensive catalogs provided by these platforms. Prior research has focused on general-domain web search, which is designed to meet a broad range of user goa…

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