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OPTIMASI KINERJA ARSITEKTUR TWO-TOWER NEURAL COLLABORATIVE FILTERING UNTUK PENANGANAN COLD START PADA DATA IMPLICIT FEEDBACK E-COMMERCE
This study aims to develop and evaluate an efficient hybrid Neural Collaborative Filtering (NCF) architecture to address three major challenges in e-commerce recommendation systems: cold start, data sparsity, and computational efficiency. The research method used is quantitative experimental by developing a Two-Tower architecture model that integrates user-item interaction data from public datasets with additional features in the form of user demographic data and item category simulations. To address the computational challenges of large-scale data, a data pipeline optimization strategy is applied through the pre-generation negative sampling technique. Model performance was comprehensively evaluated in warm start, user cold start, and item cold start scenarios using the Hit Ratio@10 (HR@10) and Normalized Discounted Cumulative Gain (NDCG@10) metrics, and further validated through qualitative analysis using hold-out data. The results of the study show that the proposed architecture successfully provides an effective solution. First, the pipeline optimization strategy has been proven to drastically reduce training time from more than 2 hours to around 5-7 minutes per epoch on consumer-grade GPUs. Second, the hybrid model has successfully overcome the cold start problem by increasing HR@10 from 0.0053 (baseline) to 0.0096, representing an 81% improvement in performance. Third, in the user cold start scenario, despite a trade-off in the precision ranking metric, qualitative validation proves that the model has good generalization capabilities by achieving a category relevance rate of 65% for new users who have never interacted before.
Fakultas Teknik Universitas Sultan Ageng Tirtayasa
Informatika, Fakultas Teknik Universitas Sultan Ageng Tirtayasa
OPTIMASI KINERJA ARSITEKTUR TWO-TOWER NEURAL COLLABORATIVE FILTERING UNTUK PENANGANAN COLD START PADA DATA IMPLICIT FEEDBACK E-COMMERCE (Text)
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QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)