Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms. However, the two tools often operate independently, failing to combine the strengths of recommender systems to accurately capture user tastes with search systems' ability to process user queries. We propose a novel remedy to this problem by automatically recommending personalized fashion items based on a user-provided text request. Our proposed model, WhisperLite, uses contrastive learning to capture user intent from natural language text and improves the recommendation quality of fashion products. WhisperLite combines the strength of CLIP embeddings with additional neural network layers for personalization, and is trained using a composite loss function based on binary cross entropy and contrastive loss. The model demonstrates a significant improvement in offline recommendation retrieval metrics when tested on a real-world dataset collected from an online retail fashion store, as well as widely used open-source datasets in different e-commerce domains, such as restaurants, movies and TV shows, clothing and shoe reviews. We additionally conduct a user study that captures user judgements on the relevance of the model's recommended items, confirming the relevancy of WhisperLite's recommendations in an online setting.
翻译:推荐系统和搜索对于促进个人化和方便在线时装平台浏览都是不可或缺的,但这两个工具往往独立运作,未能将推荐系统的力量结合起来,以准确捕捉用户的口味和搜索系统处理用户查询的能力来准确捕捉用户的口味。我们建议根据用户提供的文本请求,自动推荐个性化时装项目,以此解决这个问题。我们提议的模型WhisperLite(WhisperLite)利用对比学习来捕捉自然语言文本中的用户意图,提高时装产品的建议质量。WhisperLite(WhisperLite)将CLIP嵌入的强度与额外的个人化神经网络层结合起来,并且利用基于二进制交叉和对比损失的综合损失功能进行培训。该模型表明,在对网上零售时装店收集的真实世界数据集进行测试时,以及在不同电子商务领域,例如餐馆、电影和电视节目、服装和鞋类审查中广泛使用的公开源数据集,离线检索了离线检索指标,从而大大改进了离线检索指标。我们还进行了一项用户研究,根据模型建议在线设置项目的关联性,证实了用户判断。