论文标题
个人社交媒体数据的个性化时尚推荐：项目对设定的度量学习方法
Personalized Fashion Recommendation from Personal Social Media Data: An Item-to-Set Metric Learning Approach
论文作者
论文摘要
随着在线购物时尚产品的增长，准确的时尚推荐已成为一个关键问题。同时，社交网络为个性化时尚分析提供了开放的新数据源。在这项工作中，我们研究了社交媒体数据中的个性化时尚推荐问题，即向适合其时尚偏好的社交媒体用户推荐新服装。为此，我们提出了一个项目对设定的度量学习框架，该框架学会了计算用户的一组历史时尚项目与新时尚项目之间的相似性。为了从多模式的街道视图时尚项目中提取功能，我们提出了一个嵌入模块，该模块可以执行多模式特征提取和跨模式门控融合。为了验证我们的方法的有效性，我们收集了一个现实世界中的社交媒体数据集。收集到的数据集的广泛实验表明我们提出的方法的出色表现。
With the growth of online shopping for fashion products, accurate fashion recommendation has become a critical problem. Meanwhile, social networks provide an open and new data source for personalized fashion analysis. In this work, we study the problem of personalized fashion recommendation from social media data, i.e. recommending new outfits to social media users that fit their fashion preferences. To this end, we present an item-to-set metric learning framework that learns to compute the similarity between a set of historical fashion items of a user to a new fashion item. To extract features from multi-modal street-view fashion items, we propose an embedding module that performs multi-modality feature extraction and cross-modality gated fusion. To validate the effectiveness of our approach, we collect a real-world social media dataset. Extensive experiments on the collected dataset show the superior performance of our proposed approach.
