风格能有多大？解决高维度的风格推荐问题
How big can style be? Addressing high dimensionality for recommending with style
摘要：
在推荐系统中，使用嵌入作为产品的表示是很常见的，无论是通过提取文本描述，用户会话，协作关系，或产品图像的语义嵌入。在本文中，我们提出了一种方法来提取用于时尚推荐系统中的风格嵌入，特别关注的风格信息，如纹理，打印，材料等使用这样一种类型的嵌入的主要问题是它的高维。因此，我们提出了特征减少解决方案，同时调查其在基于其主图像推荐相同风格产品的整体任务中的影响。我们提出的特征缩减允许将嵌入向量从600k特征减少到512，从而在不严重影响推荐质量的情况下减少99.91%的内存。
英文摘要：
Using embeddings as representations of products is quite commonplace in recommender systems, either by extracting the semantic embeddings of text descriptions, user sessions, collaborative relationships, or product images. In this paper, we present an approach to extract style embeddings for using in fashion recommender systems, with a special focus on style information such as textures, prints, material, etc. The main issue of using such a type of embeddings is its high dimensionality. So, we propose feature reduction solutions alongside the investigation of its influence in the overall task of recommending products of the same style based on their main image. The feature reduction we propose allows for reducing the embedding vector from 600k features to 512, leading to a memory reduction of 99.91\% without critically compromising the quality of the recommendations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
