Asian Journal of Research in Computer Science

This study aimed to develop a fashion recommendation system that classifies clothing items and recommends complementary outfit pieces based on user-selected style preferences, addressing the limitation of existing systems that rely solely on visual similarity without incorporating style-based personalisation. The study was carried out at the Department of Information and Communication Technology, Faculty of Humanities and Social Sciences, University of Sri Jayewardenepura, Sri Lanka, between 25^th July 2025 and 2^nd June 2026, and followed a quantitative experimental approach involving the design, development, and performance evaluation of a two-stage deep learning-based classification and recommendation system. The proposed system consists of two stages: classification and recommendation. In the first stage, the system classifies the query item using a hybrid convolutional neural network (CNN) model combining ResNet-50 and EfficientNetB0. In the second stage, Fashion CLIP and CLIP ViT-B/32 models retrieve complementary items, which are then filtered and re-ranked based on the user-selected style from four categories: Casual, Formal, Party, and Streetwear. The classification model achieved 94% accuracy on the main dataset and 89.17% on external validation, while the recommendation pipeline achieved a mean Precision@5 of 84.2% and Accuracy@5 of 94.4%. The proposed system achieved 100% style consistency compared with 56.2% for the baseline model. The proposed two-stage system combines item classification with style-aware recommendation and has practical potential for integration into fashion e-commerce platforms to enhance user experience and support cross-category sales.