cspc online dataset repository with an integrated recommender system using content based filtering algorithm Estallo, Joshua P. creator author. Quiapo, Janben Aldrich H. author. Parañal, Jan Dale M. author. Onesa, Rosel O. adviser. Fortuno, Kaela Marie N. chair, panelist. Pandes, Tiffany Lyn O. panelist. Ibo, Allan Jr., O. panelist. text bibliography ph 2023 monographic eng

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101 leaves. The significance of data collection in research is crucial, as it provides essential in-formation for hypothesis testing and drawing meaningful conclusions. Novice researchers often face challenges in accessing relevant datasets, impacting their work significantly due to issues like accessibility, completeness, and biases within datasets, leading to errors. This study aims to enhance user experience by developing a prototype dataset repository using content-based filtering algorithm on a centralized web platform. Utilizing a Python pro-gram with TF-IDF and Cosine Similarity, the system achieved a high user rating of 0.9481, effectively handling file uploads and resolving dataset query indexing. With an average pre-cision and recall score of 0.60, the system accurately predicts and delivers relevant datasets, emphasizing the precision in analyzing metadata to meet user needs. Researchers advocate for future improvements, including item ratings integration and advanced natural language processing techniques, to refine the system's accuracy in recommendations based on user interactions. Joshua P. Estallo, Janben Aldrich H. Quiapo, and Jan Dale M. Parañal Undergraduate Thesis (BSCS) - Computer Science. Camarines Sur Polytechnic Colleges, 2023. Includes bibliographic references. CSPC 250814 20250814153018.0 eng