%0 Conference Paper %1 Petridis_2022 %A Petridis, Savvas %A Daskalova, Nediyana %A Mennicken, Sarah %A Way, Samuel F %A Lamere, Paul %A Thom, Jennifer %B 27th International Conference on Intelligent User Interfaces %D 2022 %I ACM %K information-visualization interactive-recommender iui2022 %P 120-133 %R 10.1145/3490099.3511156 %T TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems %U https://doi.org/10.1145%2F3490099.3511156 %X Recommender systems are ubiquitous and influence the information we consume daily 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 this paper, we study how we can better support users in exploring a novel space, specifically focusing on music genres. Informed by interviews with expert music listeners, we developed TastePaths: an interactive web tool that helps users explore an overview of the genre-space via a graph of connected artists. We conducted a comparative user study with 16 participants where each of them used a personalized version of TastePaths (built with a set of artists the user listens to frequently) and a non-personalized one (based on a set of the most popular artists in a genre). We find that participants employed various strategies to explore the space. Overall, they greatly preferred the personalized version as it helped anchor their exploration and provided recommendations that were more compatible with their personal taste. In addition to that, TastePaths helped participants specify and articulate their interest in the genre and gave them a better understanding of the system’s organization of music. Based on our findings, we discuss opportunities and challenges for incorporating more control and expressive feedback in recommendation systems to help users explore spaces beyond their immediate interests and improve these systems’ underlying algorithms.

@inproceedings{Petridis_2022, abstract = {Recommender systems are ubiquitous and influence the information we consume daily 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 this paper, we study how we can better support users in exploring a novel space, specifically focusing on music genres. Informed by interviews with expert music listeners, we developed TastePaths: an interactive web tool that helps users explore an overview of the genre-space via a graph of connected artists. We conducted a comparative user study with 16 participants where each of them used a personalized version of TastePaths (built with a set of artists the user listens to frequently) and a non-personalized one (based on a set of the most popular artists in a genre). We find that participants employed various strategies to explore the space. Overall, they greatly preferred the personalized version as it helped anchor their exploration and provided recommendations that were more compatible with their personal taste. In addition to that, TastePaths helped participants specify and articulate their interest in the genre and gave them a better understanding of the system’s organization of music. Based on our findings, we discuss opportunities and challenges for incorporating more control and expressive feedback in recommendation systems to help users explore spaces beyond their immediate interests and improve these systems’ underlying algorithms.}, added-at = {2023-06-05T05:39:16.000+0200}, author = {Petridis, Savvas and Daskalova, Nediyana and Mennicken, Sarah and Way, Samuel F and Lamere, Paul and Thom, Jennifer}, biburl = {https://www.bibsonomy.org/bibtex/245211c116e05cfc501e356d95c83f292/brusilovsky}, booktitle = {27th International Conference on Intelligent User Interfaces}, description = {TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems | 27th International Conference on Intelligent User Interfaces}, doi = {10.1145/3490099.3511156}, interhash = {8b31083ce9094a6fec034fa6da685ef0}, intrahash = {45211c116e05cfc501e356d95c83f292}, keywords = {information-visualization interactive-recommender iui2022}, month = mar, pages = {120-133}, publisher = {{ACM}}, timestamp = {2023-06-05T05:39:16.000+0200}, title = {{TastePaths}: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems}, url = {https://doi.org/10.1145%2F3490099.3511156}, year = 2022 }