Modeling Product Search Relevance in e-Commerce
This work addresses the gap between customer desires and search suggestions in e-commerce, but it appears incremental as it compares existing methods without introducing a novel approach.
The paper tackled the problem of predicting product search relevance in e-commerce by comparing conventional information retrieval models like BM25 and Indri with deep learning models such as word2vec, sentence2vec, and paragraph2vec, but it did not report specific numerical results.
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.