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Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning (2402.16299v1)

Published 26 Feb 2024 in cs.IR and cs.LG

Abstract: Recommender systems serve a dual purpose for users: sifting out inappropriate or mismatched information while accurately identifying items that align with their preferences. Numerous recommendation algorithms are designed to provide users with a personalized array of information tailored to their preferences. Nevertheless, excessive personalization can confine users within a "filter bubble". Consequently, achieving the right balance between accuracy and diversity in recommendations is a pressing concern. To address this challenge, exemplified by music recommendation, we introduce the Diversified Weighted Hypergraph music Recommendation algorithm (DWHRec). In the DWHRec algorithm, the initial connections between users and listened tracks are represented by a weighted hypergraph. Simultaneously, associations between artists, albums and tags with tracks are also appended to the hypergraph. To explore users' latent preferences, a hypergraph-based random walk embedding method is applied to the constructed hypergraph. In our investigation, accuracy is gauged by the alignment between the user and the track, whereas the array of recommended track types measures diversity. We rigorously compared DWHRec against seven state-of-the-art recommendation algorithms using two real-world music datasets. The experimental results validate DWHRec as a solution that adeptly harmonizes accuracy and diversity, delivering a more enriched musical experience. Beyond music recommendation, DWHRec can be extended to cater to other scenarios with similar data structures.

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References (64)
  1. Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering, 24, 896–911. doi:10.1109/TKDE.2011.15.
  2. Fab: Content-based, collaborative recommendation. Communications of the ACM, 40, 66–72.
  3. Does offline political segregation affect the filter bubble? an empirical analysis of information diversity for dutch and turkish twitter users. Computers in Human Behavior, 41, 405–415. doi:10.1016/j.chb.2014.05.028.
  4. Improving recommendation diversity. In Proceedings of the Conference on Artificial Intelligence and Cognitive Science (pp. 141–152). volume 85.
  5. Music recommendation by unified hypergraph: Combining social media information and music content. In Proceedings of the ACM International Conference on Multimedia (pp. 391–400). doi:10.1145/1873951.1874005.
  6. A music recommendation system based on acoustic features and user personalities. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 203–213). volume 9794. doi:10.1007/978-3-319-42996-0_17.
  7. Exploiting music play sequence for music recommendation. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 3654–3660). volume 17.
  8. Curkovic, M. (2019). Need for controlling of the filter bubble effect. Science and Engineering Ethics, 25, 323. doi:10.1007/s11948-017-0005-1.
  9. Dahlgren, P. M. (2021). A critical review of filter bubbles and a comparison with selective exposure. Nordicom Review, 42, 15–33. doi:10.2478/nor-2021-0002.
  10. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems, 22, 143–177. doi:10.1145/963770.963776.
  11. Impact of listening behavior on music recommendation. In Proceedings of the International Society for Music Information Retrieval (pp. 483–488).
  12. Recommender systems and their impact on sales diversity. In Proceedings of the ACM Conference on Electronic Commerce (pp. 192–199). doi:10.1145/1250910.1250939.
  13. Using social tags to infer context in hybrid music recommendation. In Proceedings of the International Workshop on Web Information and Data Management (pp. 41–48). doi:10.1145/2389936.2389946.
  14. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 639–648). doi:10.1145/3397271.3401063.
  15. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (pp. 173–182). doi:10.1145/3038912.3052569.
  16. Benefits of diverse news recommendations for democracy: A user study. Digital Journalism, (pp. 1–21). doi:10.1080/21670811.2021.2021804.
  17. Collaborative filtering for implicit feedback datasets. In Proceedings of the IEEE International Conference on Data Mining (pp. 263–272). doi:10.1109/ICDM.2008.22.
  18. Item diversified recommendation based on influence diffusion. Information Processing & Management, 56, 939–954. doi:10.1016/j.ipm.2019.01.006.
  19. An audio recommendation system based on audio signature description scheme in mpeg-7 audio. In Proceedings of the IEEE International Conference on Multimedia and Expo (pp. 639–642). volume 1. doi:10.1109/ICME.2004.1394273.
  20. Algorithmic personalization of source cues in the filter bubble: Self-esteem and self-construal impact information exposure. New Media & Society, 25, 2095–2117. doi:10.1177/14614448211027963.
  21. Matrix factorization techniques for recommender systems. Computer, 42, 30–37. doi:10.1109/MC.2009.263.
  22. Music recommendation via hypergraph embedding. IEEE Transactions on Neural Networks and Learning Systems, 34, 7887–7899. doi:10.1109/TNNLS.2022.3146968.
  23. A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design. Knowledge-Based Systems, 215, 106739. doi:10.1016/j.knosys.2021.106739.
  24. Long-tail session-based recommendation. In Proceedings of the ACM Conference on Recommender Systems (pp. 509–514). doi:10.1145/3383313.3412222.
  25. Interactive recommending with tag-enhanced matrix factorization (tagmf). International Journal of Human-Computer Studies, 121, 21–41. doi:10.1016/j.ijhcs.2018.05.002.
  26. A diversity adjusting strategy with personality for music recommendation. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (pp. 7–14).
  27. Research on diversity and accuracy of the recommendation system based on multi-objective optimization. Neural Computing and Applications, 35, 5155–5163. doi:10.1007/s00521-020-05438-w.
  28. Music recommendation using graph based quality model. Signal Processing, 120, 806–813. doi:10.1016/j.sigpro.2015.03.026.
  29. Multiobjective e-commerce recommendations based on hypergraph ranking. Information Sciences, 471, 269–287. doi:10.1016/j.ins.2018.07.029.
  30. An introduction to the five-factor model and its applications. Journal of Personality, 60, 175–215. doi:10.1111/j.1467-6494.1992.tb00970.x.
  31. What are filter bubbles really? a review of the conceptual and empirical work. In Proceedings of the ACM Conference on User Modeling, Adaptation and Personalization (pp. 274–279). doi:10.1145/3511047.3538028.
  32. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, . doi:10.48550/arXiv.1301.3781.
  33. Exploiting similarities among languages for machine translation. CoRR, abs/1309.4168. doi:10.48550/arXiv.1309.4168.
  34. Content-based music recommendation system. In Proceedings of the Conference of Open Innovations Association (pp. 274–279). doi:10.23919/FRUCT52173.2021.9435533.
  35. Sound and music recommendation with knowledge graphs. ACM Transactions on Intelligent Systems and Technology, 8, 1–21. doi:10.1145/2926718.
  36. Pariser, E. (2011). The Filter Bubble: What the Internet Is Hiding from You. Penguin UK.
  37. Technical note–online hypergraph matching with delays. Operations Research, 70, 2194–2212. doi:10.1287/opre.2022.2277.
  38. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 701–710). doi:10.1145/2623330.2623732.
  39. A popularity-based recommendation system using machine learning. In Proceedings of the Machine Learning in Information and Communication Technology (pp. 143–150). doi:10.1007/978-981-19-5090-2_14.
  40. Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618, . doi:10.48550/arXiv.1205.2618.
  41. Bursting your (filter) bubble: Strategies for promoting diverse exposure. In Proceedings of the Conference on Computer Supported Cooperative Work Companion (pp. 95–100). doi:10.1145/2441955.2441981.
  42. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (pp. 175–186). doi:10.1145/192844.192905.
  43. User insights on diversity in music recommendation lists. In Proceedings of the International Society for Music Information Retrieval (pp. 446–453).
  44. Rong, X. (2014). Word2vec parameter learning explained. CoRR, abs/1411.2738. doi:10.48550/arXiv.1411.2738.
  45. Deep reinforcement learning-based music recommendation with knowledge graph using acoustic features. ITE Transactions on Media Technology and Applications, 10, 8–17. doi:10.3169/mta.10.8.
  46. Tailoring music recommendations to users by considering diversity, mainstreaminess, and novelty. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 947–950). doi:10.1145/2766462.2767763.
  47. Emotion-based music recommendation by affinity discovery from film music. Expert Systems with Applications, 36, 7666–7674. doi:10.1016/j.eswa.2008.09.042.
  48. Music recommendation based on acoustic features and user access patterns. IEEE Transactions on Audio, Speech, and Language Processing, 17, 1602–1611. doi:10.1109/TASL.2009.2020893.
  49. Using rich social media information for music recommendation via hypergraph model. ACM Transactions on Multimedia Computing, Communications, and Applications, 7S, 1–22. doi:10.1145/2037676.2037679.
  50. Music recommendation using hypergraphs and group sparsity. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 56–60). doi:10.1109/ICASSP.2013.6637608.
  51. Attention-based dynamic user modeling and deep collaborative filtering recommendation. Expert Systems with Applications, 188, 116036. doi:10.1016/j.eswa.2021.116036.
  52. Examining the interactive effects of the filter bubble and the echo chamber on radicalization. Journal of Experimental Criminology, 19, 119–141. doi:10.1007/s11292-021-09471-0.
  53. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering, 35, 4425–4445. doi:10.1109/TKDE.2022.3145690.
  54. Improving accuracy and diversity in matching of recommendation with diversified preference network. IEEE Transactions on Big Data, 8, 955–967. doi:10.1109/TBDATA.2021.3103263.
  55. Towards a more user-friendly and easy-to-use benchmark library for recommender systems. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2837–2847). doi:10.1145/3539618.3591889.
  56. Deep matrix factorization models for recommender systems. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 3203–3209). volume 17. doi:10.24963/ijcai.2017/447.
  57. Revisiting user mobility and social relationships in lbsns: A hypergraph embedding approach. In Proceedings of the World Wide Web Conference (pp. 2147–2157). doi:10.1145/3308558.3313635.
  58. Dgrec: Graph neural network for recommendation with diversified embedding generation. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining (pp. 661–669). doi:10.1145/3539597.3570472.
  59. Group identification via transitional hypergraph convolution with cross-view self-supervised learning. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 2969–2979). doi:10.1145/3583780.3614902.
  60. Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on linked open data. Knowledge-Based Systems, 252, 109333. doi:10.1016/j.knosys.2022.109333.
  61. Dynamic graph neural networks for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering, 35, 4741–4753. doi:10.1109/TKDE.2022.3151618.
  62. An improved user-based movie recommendation algorithm. In Proceedings of the IEEE International Conference on Computer and Communications (pp. 874–877). doi:10.1109/CompComm.2016.7924828.
  63. Recbole 2.0: Towards a more up-to-date recommendation library. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 4722–4726). doi:10.1145/3511808.3557680.
  64. Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 4653–4664). doi:10.1145/3459637.3482016.

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