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@article{206798,
        author = {Sharada HP and Dr. Sandeep Bhat},
        title = {Fashion Fusion: Deep Learning-Based Outfit Recommendation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {486-490},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206798},
        abstract = {The increased growth of online fashion platforms expands the desire of smart systems that will provide personalized styling recommendations. This paper gives a deep learning–based framework for generating fashion outfit suggestions leads to individual user preferences. The present system tries to understand the compatibility between clothing products while all past methods were trying to find the compatibility based on similarity of the cloths and customer purchase history. There are different types of CNN models but here we use pre-trained CNN Resnet50. Shared space embedding and metric learning are the main two techniques which takes major role in identifying matching and non-matching outfits. All specific parts of the image are identified by attention mechanism. These attention mechanisms are also used to focus on relevant style parts. Experiments shows that the given method effectively calculate difficult relationships between fashion items and gives more relevant and visually matching things compared to conventional methods. So, this generated system can be applied in e-commerce and fashion applications to improve user experience by providing fully automatic, smart, and personalized styling assistance.},
        keywords = {Complementary Product Recommendation, cross domain recommendation visual compatibility learning, feature extraction, Triplet’s loss.},
        month = {July},
        }