论文标题
在电子商务中建模产品搜索相关性
Modeling Product Search Relevance in e-Commerce
论文作者
论文摘要
随着电子商务的快速增长，在线产品搜索已成为一种流行而有效的范式，供客户找到所需的产品并从事在线购物。但是，这些产品之间仍然存在很大的差距，客户真正希望购买和相关性的产品，这些产品是根据客户的查询而建议的。在本文中，我们建议使用涉及机器学习，自然语言处理和信息检索的技术来预测给定搜索查询和产品的相关性分数的强大方法。我们将常规信息检索模型（例如BM25和Indri）与Word2Vec，Sente2Vec和Paragraph2Vec等深度学习模型进行了比较。我们从实验中分享了一些见解和发现。
With the rapid growth of e-Commerce, online product search has emerged as a popular and effective paradigm for customers to find desired products and engage in online shopping. However, there is still a big gap between the products that customers really desire to purchase and relevance of products that are suggested in response to a query from the customer. In this paper, we propose a robust way of predicting relevance scores given a search query and a product, using techniques involving machine learning, natural language processing and information retrieval. We compare conventional information retrieval models such as BM25 and Indri with deep learning models such as word2vec, sentence2vec and paragraph2vec. We share some of our insights and findings from our experiments.
