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.
For any machine learning technique, features used to train a model typically have a critical influence on the performance of the deployed algorithm. Features derived from user events collected by publishers are often used in predicting the user's propensity to click or purchase @cite_4 . However, these features represent only a weak proxy to what publishers and advertisers are actually interested in, namely, user's purchase intent. On the other hand, commercial e-mails in the form of promotions and purchase receipts convey a strong, very direct purchase intent signal that can enable advertisers to reach a high-quality audience. According to the Direct Marketing Association's National Client E-mail Report" www.powerprodirect.com statistics, accessed June 2015 , an e-mail user has been identified as having over $10 recent work @cite_10 was among the first attempts to investigate the value of commercial e-mail data. The authors applied Sparse Principal Component Analysis (SPCA) on the counts of received e-mails in order to cluster commercial domains, demonstrating significant promise of e-mail data source.
@cite_10 @cite_4
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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. With Web mail services offering larger and larger storage capacity, most users do not feel the need to systematically delete messages anymore and inboxes keep growing. It is quite surprising that in spite of the huge progress of relevance ranking in Web Search, mail search results are still typically ranked by date. This can probably be explained by the fact that users demand perfect recall in order to "re-find" a previously seen message, and would not trust relevance ranking. Yet mail search is still considered a difficult and frustrating task, especially when trying to locate older messages. In this paper, we study the current search traffic of Yahoo mail, a major Web commercial mail service, and discuss the limitations of ranking search results by date. We argue that this sort-by-date paradigm needs to be revisited in order to account for the specific structure and nature of mail messages, as well as the high-recall needs of users. We describe a two-phase ranking approach, in which the first phase is geared towards maximizing recall and the second phase follows a learning-to-rank approach that considers a rich set of mail-specific features to maintain precision. We present our results obtained on real mail search query traffic, for three different datasets, via manual as well as automatic evaluation. We demonstrate that the default time-driven ranking can be significantly improved in terms of both recall and precision, by taking into consideration time recency and textual similarity to the query, as well as mail-specific signals such as users' actions. E-commerce has revolutionised how we browse and purchase products and services globally. However, with revolution comes disruption as retailers and users struggle to keep up with the pace of change. Retailers are increasingly using a varied number of machine learning techniques in areas such as information retrieval, user interface design, product catalogue curation and sentiment analysis, all of which must operate at scale and in near real-time. Understanding user purchase intent is important for a number of reasons. Buyers typically represent <5 of all e-commerce users, but contribute virtually all of the retailer profit. Merchants can cost-effectively target measures such as discounting, special offers or enhanced advertising at a buyer cohort - something that would be cost prohibitive if applied to all users. We used supervised classic machine learning and deep learning models to infer user purchase intent from their clickstreams. Our contribution is three-fold: first we conducted a detailed analysis of explicit features showing that four broad feature classes enable a classic model to infer user intent. Second, we constructed a deep learning model which recovers over 98 of the predictive power of a state-of-the-art approach. Last, we show that a standard word language deep model is not optimal for e-commerce clickstream analysis and propose a combined sampling and hidden state management strategy to improve the performance of deep models in the e-commerce domain. We also propose future work in order to build on the results obtained. A long-standing goal in advertising is to reduce wasted costs due to advertising to people who are unlikely to buy, as well as to those who would make a purchase whether they saw an ad or not. The ideal audience for the advertiser are those incremental users who would buy if shown an ad, and would not buy, if not shown the ad. On the other hand, for publishers who are paid when the user clicks or buys, revenue may be maximized by showing ads to those users who are most likely to click or purchase. We show analytically and empirically that an optimization towards one metric might result in an inferior performance in the other one. We present a novel algorithm, called SLC, that performs a joint optimization towards both advertisers' and publishers' goals and provides superior results in both.