@inproceedings{72b6ba316dc247acb4b59294e641fb4b,
title = "ArtEx: A User-Controllable Web Interface for Visual Art Recommendations",
abstract = "We introduce a web-based interface for visual art recommendations, empowering users to adjust popularity and diversity through intuitive sliders. Built on the SemArt dataset and leveraging multimodal BLIP features, ArtEx allows users to fine-tune recommendations across dimensions like genre, time period, and artist. This demo paper presents ArtEx's interactive interface, showcasing its ability to enhance user engagement and satisfaction through transparent, user-driven personalization.",
keywords = "Artwork, Machine Learning, Personalization, Recommendation, User Experience",
author = "Hendrawan, \{Rully Agus\} and Peter Brusilovsky and Leiva, \{Luis A.\} and Yilma, \{Bereket A.\}",
note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 19th ACM Conference on Recommender Systems, RecSys 2025 ; Conference date: 22-09-2025 Through 26-09-2025",
year = "2025",
month = aug,
day = "7",
doi = "10.1145/3705328.3759343",
language = "English",
series = "RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "1328--1330",
booktitle = "RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems",
}