Abstract
Among the enormous volume of data available, the recommender system assists users in locating useful information that meets their needs. Video and audio recordings are complex media and linking datasets that satisfy defined requirements is becoming an increasingly significant, but challenging endeavor. As part of this paper, we propose a data linkage approach for matching audio recordings to related music videos. A cooperative method was introduced utilizing the Elasticsearch search engine primarily for preprocessing. Data features were further aggregated using text data matching scores, date and time features, popularity scores, and data completeness scores. We automated the machine learning process using PyCaret, which gave us more time for analysis and less time for coding. Experiments demonstrate that this method can generate a ranking of significant features and performance tracking that improves the efficacy of data linking.
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Huynh, L.D., Huy, P.Q., Hung, P.D., Diep, V.T. (2023). Video and Audio Linkage in Recommender System. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2023. Lecture Notes in Computer Science, vol 14166. Springer, Cham. https://doi.org/10.1007/978-3-031-43815-8_18
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