当前位置: X-MOL 学术arXiv.cs.IR › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Modeling Product Search Relevance in e-Commerce
arXiv - CS - Information Retrieval Pub Date : 2020-01-14 , DOI: arxiv-2001.04980
Rahul Radhakrishnan Iyer, Rohan Kohli, Shrimai Prabhumoye

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.

中文翻译:

电子商务中的产品搜索相关性建模

随着电子商务的快速发展,在线产品搜索已成为客户寻找所需产品并进行在线购物的流行且有效的范式。然而,客户真正希望购买的产品与响应客户查询而建议的产品的相关性之间仍然存在很大差距。在本文中,我们提出了一种强大的方法来预测给定搜索查询和产品的相关性分数,使用涉及机器学习、自然语言处理和信息检索的技术。我们将 BM25 和 Indri 等传统信息检索模型与 word2vec、sentence2vec 和 Paragraph2vec 等深度学习模型进行了比较。我们分享了我们从实验中获得的一些见解和发现。
更新日期:2020-01-16
down
wechat
bug