2000 character limit reached
EXPLORE -- Explainable Song Recommendation (2401.00353v1)
Published 30 Dec 2023 in cs.IR
Abstract: This study explores the development of an explainable music recommendation system with enhanced user control. Leveraging a hybrid of collaborative filtering and content-based filtering, we address the challenges of opaque recommendation logic and lack of user influence on results. We present a novel approach combining advanced algorithms and an interactive user interface. Our methodology integrates Spotify data with user preference analytics to tailor music suggestions. Evaluation through RMSE and user studies underscores the efficacy and user satisfaction with our system. The paper concludes with potential directions for future enhancements in group recommendations and dynamic feedback integration.
- Behnoush Abdollahi and Olfa Nasraoui. 2017. Using Explainability for Constrained Matrix Factorization. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 79–83. https://doi.org/10.1145/3109859.3109913
- Music Recommender System Based on Genre using Convolutional Recurrent Neural Networks. Procedia Computer Science 157 (2019), 99–109. https://doi.org/10.1016/j.procs.2019.08.146 The 4th International Conference on Computer Science and Computational Intelligence (ICCSCI 2019) : Enabling Collaboration to Escalate Impact of Research Results for Society.
- A reliable deep representation learning to improve trust-aware recommendation systems. Expert Systems with Applications 197 (2022), 116697. https://doi.org/10.1016/j.eswa.2022.116697
- Latent Factor Interpretations for Collaborative Filtering. CoRR abs/1711.10816 (2017). arXiv:1711.10816 http://arxiv.org/abs/1711.10816
- Designing Explanations for Group Recommender Systems. CoRR abs/2102.12413 (2021). arXiv:2102.12413 https://arxiv.org/abs/2102.12413
- ISMIR 2017. The Music Listening Histories Dataset (MLHD). Retrieved Nov 06, 2022 from https://archives.ismir.net/ismir2017/paper/000180.pdf
- Advanced Recommendation Systems Through Deep Learning. In Proceedings of the 3rd International Conference on Networking, Information Systems &; Security (Marrakech, Morocco) (NISS2020). Association for Computing Machinery, New York, NY, USA, Article 51, 8 pages. https://doi.org/10.1145/3386723.3387870
- A Survey on Visualizations for Musical Data. Computer Graphics Forum 39, 6 (2020), 82–110. https://doi.org/10.1111/cgf.13905 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.13905
- Seungyeon Lee and Dohyun Kim. 2022. Deep learning based recommender system using cross convolutional filters. Information Sciences 592 (2022), 112–122.
- Yu Liang and Martijn C. Willemsen. 2019. Personalized Recommendations for Music Genre Exploration. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (Larnaca, Cyprus) (UMAP ’19). Association for Computing Machinery, New York, NY, USA, 276–284. https://doi.org/10.1145/3320435.3320455
- Yu Liang and Martijn C. Willemsen. 2021. Interactive Music Genre Exploration with Visualization and Mood Control. In 26th International Conference on Intelligent User Interfaces (College Station, TX, USA) (IUI ’21). Association for Computing Machinery, New York, NY, USA, 175–185. https://doi.org/10.1145/3397481.3450700
- “Knowing Me, Knowing You”: Personalized Explanations for a Music Recommender System. User Modeling and User-Adapted Interaction 32, 1–2 (apr 2022), 215–252. https://doi.org/10.1007/s11257-021-09304-9
- Judith Masthoff. 2011. Group Recommender Systems: Combining Individual Models. Springer US, Boston, MA, 677–702. https://doi.org/10.1007/978-0-387-85820-3_21
- Michael J. McGuffin. 2012. Simple algorithms for network visualization: A tutorial. Tsinghua Science and Technology 17, 4 (2012), 383–398. https://doi.org/10.1109/TST.2012.6297585
- Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (Yokohama, Yokohama, Japan) (IJCAI’20). Article 373, 7 pages.
- Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval 14, 1 (2020), 1–101.