时尚推荐系统

M. Vinitha, Dr.B. Nagarajanaik, Mallikarjuna Nandi, C. Naga, Sri Charan, K. Priyanka
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引用次数: 1

摘要

时尚推荐系统在电子商务行业越来越重要,它为用户提供个性化的服装建议,提升用户的购物体验,并促进销售。本文介绍了一种结合机器学习和深度学习技术的时尚推荐新方法。我们利用用户偏好和时尚商品的综合数据集来创建一个强大的推荐系统。我们的方法首先采用协同过滤和矩阵因式分解方法来建立用户与商品之间的交互。随后,利用神经协同过滤和递归神经网络等深度学习模型来捕捉时尚数据中错综复杂的模式。这种组合使系统能够根据用户的历史选择、风格和实时行为提供个性化的时尚推荐。对我们系统的评估表明,该系统能有效提高用户参与度和满意度,同时增加平台收入。所提出的时尚推荐系统展示了在不断发展的时尚电子商务环境中整合机器学习和深度学习以优化个性化时尚建议的潜力。这项研究为推荐系统及其在时尚行业的应用这一更广阔的领域做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fashion Recommendation System
Fashion recommendation systems have become increasingly essential in the e-commerce industry, providing personalized outfit suggestions to users, enhancing their shopping experience, and boosting sales. This paper presents a novel approach to fashion recommendation by combining machine learning and deep learning techniques. We leverage a comprehensive dataset of user preferences and fashion items to create a robust recommendation system. Our approach first employs collaborative filtering and matrix factorization methods to establish user-item interactions. Subsequently, deep learning models, such as neural collaborative filtering and recurrent neural networks, are utilized to capture intricate patterns within the fashion data. This combination enables the system to offer personalized fashion recommendations based on the user's historical choices, style, and real-time Behaviour. The evaluation of our system demonstrates its effectiveness in enhancing user engagement and satisfaction while increasing the platform's revenue. The proposed fashion recommendation system showcases the potential of integrating machine learning and deep learning for optimizing personalized fashion suggestions in the ever- evolving fashion e-commerce landscape. This research contributes to the broader field of recommendation systems and their applications in the fashion industry.
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