DATA-DRIVEN FASHION: ENHANCING CONSUMER DECISIONS THROUGH TREND, PRICE, AND RATING ANALYSIS

Abstract

Due to the swift growth of e-commerce, an accurate and context-aware recommenda tion system is required, especially in the fashion sector where users’ preferences are related to both unique visual traits and categorical features. Most fashion retrieval approaches are based on either visual similarity or solely text-based metadata, but they do not account for the multi-dimensionality of fashion objects. This thesis introduces a novel Hybrid Rec ommendation System Architecture to overcome the semantic gap between visual aspect and contextual information. The proposed method uses a two-pass extraction approach. Global max pooling and L2 normalization are applied to these features to obtain robust image embeddings with a pre-trained ResNet50 deep learning backbone. At the same time, a categorical metadata pipeline performs sparse one-hot encoding of explicit item attributes.A categorical meta data pipeline is also performed concurrently, using sparse one-hot encoding of explicit item attributes. These two unique sets of features are fused with a customisable weighted fusion algorithm, which can be fine-tuned for the visual and textual significance. The system architecture also features an optimized o!ine serialization process for the system to be usable in the real world while maintaining low latency retrieval. Evidence shows that the proposed hybrid approach outperforms unimodal baseline approaches. Comparative ablation, in which all other methods were disabled except the hybrid model, yielded an outstanding Precision@5 score of 94%, which outperforms the visual-only retrieval and metadata-only retrieval. Overall, this study o”ers a scalable and e#cient platform that can be used in the modern web infrastructure, enhancing product discovery and automated fashion curation for users.