{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/e-commerce-in-your-inbox-product","title":"E-commerce in Your Inbox: Product Recommendations at Scale","arxiv_id":"1606.07154","date":"2016-06-23","proceeding":null,"authors":["Mihajlo Grbovic","Vladan Radosavljevic","Nemanja Djuric","Narayan Bhamidipati","Jaikit Savla","Varun Bhagwan","Doug Sharp"],"abstract":"In recent years online advertising has become increasingly ubiquitous and\neffective. Advertisements shown to visitors fund sites and apps that publish\ndigital content, manage social networks, and operate e-mail services. Given\nsuch large variety of internet resources, determining an appropriate type of\nadvertising for a given platform has become critical to financial success.\nNative advertisements, namely ads that are similar in look and feel to content,\nhave had great success in news and social feeds. However, to date there has not\nbeen a winning formula for ads in e-mail clients. In this paper we describe a\nsystem that leverages user purchase history determined from e-mail receipts to\ndeliver highly personalized product ads to Yahoo Mail users. We propose to use\na novel neural language-based algorithm specifically tailored for delivering\neffective product recommendations, which was evaluated against baselines that\nincluded showing popular products and products predicted based on\nco-occurrence. We conducted rigorous offline testing using a large-scale\nproduct purchase data set, covering purchases of more than 29 million users\nfrom 172 e-commerce websites. Ads in the form of product recommendations were\nsuccessfully tested on online traffic, where we observed a steady 9% lift in\nclick-through rates over other ad formats in mail, as well as comparable lift\nin conversion rates. Following successful tests, the system was launched into\nproduction during the holiday season of 2014.","url_abs":"http://arxiv.org/abs/1606.07154v1","url_pdf":"http://arxiv.org/pdf/1606.07154v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"e-commerce-in-your-inbox-product","repo_url":"https://github.com/sunzhuntu/Recurrent-Knowledge-Graph-Embedding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1606.07154","atlas_url":"https://app.syntology.ai/?focus=1606.07154","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}