2015 ECommerceinYourInboxProductReco
- (Grbovic et al., 2015) ⇒ Mihajlo Grbovic, Vladan Radosavljevic, Nemanja Djuric, Narayan Bhamidipati, Jaikit Savla, Varun Bhagwan, and Doug Sharp. (2015). “E-commerce in Your Inbox: Product Recommendations at Scale.” In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2788627
Subject Headings:
Notes
Cited By
- http://scholar.google.com/scholar?q=%222015%22+E-commerce+in+Your+Inbox%3A+Product+Recommendations+at+Scale
- http://dl.acm.org/citation.cfm?id=2783258.2788627&preflayout=flat#citedby
2016
- (Vasile et al., 2016) ⇒ Flavian Vasile, Elena Smirnova, and Alexis Conneau. (2016). “Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation.” In: Proceedings of the 10th ACM Conference on Recommender Systems. ISBN:978-1-4503-4035-9 doi:10.1145/2959100.2959160
Quotes
Abstract
In recent years online advertising has become increasingly ubiquitous and effective. Advertisements shown to visitors fund sites and apps that publish digital content, manage social networks, and operate e-mail services. Given such large variety of internet resources, determining an appropriate type of advertising for a given platform has become critical to financial success. Native advertisements, namely ads that are similar in look and feel to content, have had great success in news and social feeds. However, to date there has not been a winning formula for ads in e-mail clients. In this paper we describe a system that leverages user purchase history determined from e-mail receipts to deliver highly personalized product ads to Yahoo Mail users. We propose to use a novel neural language-based algorithm specifically tailored for delivering effective product recommendations, which was evaluated against baselines that included showing popular products and products predicted based on co-occurrence. We conducted rigorous offline testing using a large-scale product purchase data set, covering purchases of more than 29 million users from 172 e-commerce websites. Ads in the form of product recommendations were successfully tested on online traffic, where we observed a steady 9% lift in click-through rates over other ad formats in mail, as well as comparable lift in conversion rates. Following successful tests, the system was launched into production during the holiday season of 2014.
References
;
Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
---|---|---|---|---|---|---|---|---|---|---|
2015 ECommerceinYourInboxProductReco | Nemanja Djuric Vladan Radosavljevic Mihajlo Grbovic Narayan Bhamidipati Jaikit Savla Varun Bhagwan Doug Sharp | E-commerce in Your Inbox: Product Recommendations at Scale | 10.1145/2783258.2788627 | 2015 |