Everyone Likes Shopping! Multi-class Product Categorization for e-Commerce

Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL, pages 1329–1333, Denver, Colorado, May 31 – June 5, 2015. c⃝2015 Association for Computational Linguistics Everyone Likes Shopping! Multi-class Product Categorization for e-Commerce Zornitsa Kozareva Yahoo! Labs 701 First Avenue Sunnyvale, CA 94089 zornitsa@kozareva.com Abstract Online shopping caters the needs of millions of users on a daily basis. To build an accurate system that can retrieve relevant products for a query like “MB252 with travel bags” one requires product and query categorization mechanisms, which classify the text Home&Garden>Kitchen&Dining>Kitchenas Appliances>Blenders. One of the biggest challenges in e-Commerce is that providers like Amazon, e-Bay, Google, Yahoo! and Walmart organize products into different product taxonomies making it hard and time-consuming for sellers to categorize goods for each shopping platform. To address this challenge, we propose an automatic product categorization mechanism, which for a given product title assigns the cor- rect product category from a taxonomy. We conducted an empirical evaluation on 445, 408 product titles and used a rich product taxon- omy of 319 categories organized into 6 lev- els. We compared performance against mul- tiple algorithms and found that the best per- forming system reaches .88 f-score. 1 Introduction and Related Work Over the past decade, e-Commerce has rapidly grown enabling customers to purchase any product with a click of a button. A key component for the success of such online shopping platforms is their ability to quickly and accurately retrieve the desired products for the customers. To be able to do so, shopping platforms use taxonomies (Kanagal et al., 2012), which hierarchically organize products from general to more specific classes. Taxonomies sup- port keyword search and guarantee consistency of the categorization of similar products, which fur- ther enables product recommendation (Ziegler et al., 2004; Weng et al., 2008) and duplicate removal. Shopping platforms like Amazon, e-Bay, Google, Yahoo!, Walmart among others use different tax- onomies to organize products making it hard and labor-intensive for sellers to categorize the products. Sometimes sellers are encouraged to find similar products to those they sell and adopt this category to their products. However, this mechanism leads to two main problems: (1) it takes a lot of time for a merchant to categorize items and (2) such taggings can be inconsistent since different sellers might cat- egorize the same product differently. To solve these problems, ideally one would like to have an auto- mated procedure, which can classify any product ti- tle into a product taxonomy. Such process will both alleviate human labor and further improve product categorization consistency in e-Commerce websites. Recently, a lot of interest has been developed around the induction of taxonomies using hierarchal LDA models (Zhang et al., 2014) and the categoriza- tion of products using product descriptions (Chen and Warren, 2013). Despite these efforts, yet no study focuses on classifying products using only ti- tles. The question we address in this paper is: Given a product title and a product taxonomy, can we ac- curately identify …

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