  Search is a prominent channel for discovering products on an e-commerce
platform. Ranking products retrieved from search becomes crucial to address
customer's need and optimize for business metrics. While learning to Rank
(LETOR) models have been extensively studied and have demonstrated efficacy in
the context of web search; it is a relatively new research area to be explored
in the e-commerce. In this paper, we present a framework for building LETOR
model for an e-commerce platform. We analyze user queries and propose a
mechanism to segment queries between broad and narrow based on user's intent.
We discuss different types of features - query, product and query-product and
discuss challenges in using them. We show that sparsity in product features can
be tackled through a denoising auto-encoder while skip-gram based word
embeddings help solve the query-product sparsity issues. We also present
various target metrics that can be employed for evaluating search results and
compare their robustness. Further, we build and compare performances of both
pointwise and pairwise LETOR models on fashion category data set. We also build
and compare distinct models for broad and narrow queries, analyze feature
importance across these and show that these specialized models perform better
than a combined model in the fashion world.
