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.
翻译:在电子商务平台上发现产品是一个突出的搜索渠道。从搜索中提取的产品排序对于满足客户的需求和优化商业计量标准至关重要。虽然对排名(LETOR)模型的学习进行了广泛研究,并展示了在网上搜索方面的功效;这是一个相对较新的研究领域,将在电子商务平台上探索。在本文中,我们提出了一个框架,用于为电子商务平台建立LETOR模型。我们分析用户查询,并提议一个机制,根据用户的意图,对广泛和狭窄的查询进行分解。我们讨论不同种类的特征——查询、产品和查询产品,并讨论使用这些特征的挑战。我们表明,产品特征的宽度可以通过解调的自动编码处理,而基于跳格的嵌入词有助于解决查询-产品宽度问题。我们还提出了各种目标指标,可用于评估搜索结果和比较其坚固性。此外,我们还在时装类数据集上建立和比较了既有精准和对称的LETROR模型的性能。我们还建立和比较了不同的模型,用于广泛和狭义查询的难度。我们展示了不同的模型,并比较了这些专业模型,这些模型比这些专业模型更能。