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.
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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. There is an alarming increase in the number of cybercrime incidents through anonymous e-mails. The problem of e-mail authorship attribution is to identify the most plausible author of an anonymous e-mail from a group of potential suspects. Most previous contributions employed a traditional classification approach, such as decision tree and Support Vector Machine (SVM), to identify the author and studied the effects of different writing style features on the classification accuracy. However, little attention has been given on ensuring the quality of the evidence. In this paper, we introduce an innovative data mining method to capture the write-print of every suspect and model it as combinations of features that occurred frequently in the suspect's e-mails. This notion is called frequent pattern, which has proven to be effective in many data mining applications, but it is the first time to be applied to the problem of authorship attribution. Unlike the traditional approach, the extracted write-print by our method is unique among the suspects and, therefore, provides convincing and credible evidence for presenting it in a court of law. Experiments on real-life e-mails suggest that the proposed method can effectively identify the author and the results are supported by a strong evidence. E-mail has become the most popular Internet application and with its rise in use has come an inevitable increase in the use of e-mail for criminal purposes. It is possible for an e-mail message to be sent anonymously or through spoofed servers. Computer forensics analysts need a tool that can be used to identify the author of such e-mail messages. This thesis describes the development of such a tool using techniques from the fields of stylometry and machine learning. An author's style can be reduced to a pattern by making measurements of various stylometric features from the text. E-mail messages also contain macro-structural features that can be measured. These features together can be used with the Support Vector Machine learning algorithm to classify or attribute authorship of e-mail messages to an author providing a suitable sample of messages is available for comparison. In an investigation, the set of authors may need to be reduced from an initial large list of possible suspects. This research has trialled authorship characterisation based on sociolinguistic cohorts, such as gender and language background, as a technique for profiling the anonymous message so that the suspect list can be reduced. Estimating a user's propensity to click on a display ad or purchase a particular item is a critical task in targeted advertising, a burgeoning online industry worth billions of dollars. Better and more accurate estimation methods result in improved online user experience, as only relevant and interesting ads are shown, and may also lead to large benefits for advertisers, as targeted users are more likely to click or make a purchase. In this paper we address this important problem, and propose an approach for improved estimation of ad click or conversion prob- ability based on a sequence of user's online actions, modeled using Hidden Conditional Random Fields (HCRF) model. In addition, in order to address the sparsity issue at the input side of the HCRF model, we propose to learn distributed, low-dimensional representations of user actions through a directed skip-gram, a neural architecture suitable for sequential data. Experimental results on a real-world data set comprising thousands of user sessions collected at Yahoo servers clearly indicate the benefits and the potential of the proposed approach, which outperformed competing state-of-the-art algorithms and obtained significant improvements in terms of retrieval measures. This paper describes an investigation of authorship gender attribution mining from e-mail text documents. We used an extended set of predominantly topic content-free e-mail document features such as style markers, structural characteristics and gender-preferential language features together with a support vector machine learning algorithm. Experiments using a corpus of e-mail documents generated by a large number of authors of both genders gave promising results for author gender categorisation. Although extremely popular, electronic commerce environments often lack information that has traditionally served to ensure trust among exchange partners. Digital technologies, however, have created new forms of "electronic word-of-mouth," which offer new potential for gathering credible information that guides consumer behaviors. We conducted a nationally representative survey and a focused experiment to assess how individuals perceive the credibility of online commercial information, particularly as compared to information available through more traditional channels, and to evaluate the specific aspects of ratings information that affect people's attitudes toward ecommerce. Survey results show that consumers rely heavily on web-based information as compared to other channels, and that ratings information is critical in the evaluation of the credibility of online commercial information. Experimental results indicate that ratings are positively associated with perceptions of product quality and purchase intention, but that people attend to average product ratings, but not to the number of ratings or to the combination of the average and the number of ratings together. Thus suggests that in spite of valuing the web and ratings as sources of commercial information, people use ratings information suboptimally by potentially privileging small numbers of ratings that could be idiosyncratic. In addition, product quality is shown to mediate the relationship between user ratings and purchase intention. The practical and theoretical implications of these findings are considered for ecommerce scholars, consumers, and vendors. Without the benefit of paralinguistic cues such as gesture, emphasis, and intonation, it can be difficult to convey emotion and tone over electronic mail (e-mail). Five experiments suggest that this limitation is often underappreciated, such that people tend to believe that they can communicate over e-mail more effectively than they actually can. Studies 4 and 5 further suggest that this overconfidence is born of egocentrism, the inherent difficulty of detaching oneself from one’s own perspective when evaluating the perspective of someone else. Because e-mail communicators “hear” a statement differently depending on whether they intend to be, say, sarcastic or funny, it can be difficult to appreciate that their electronic audience may not. In this paper, we investigate the topic of gender identification for short length, multi-genre, content-free e-mails. We introduce for the first time (to our knowledge), psycholinguistic and gender-linked cues for this problem, along with traditional stylometric features. Decision tree and Support Vector Machines learning algorithms are used to identify the gender of the author of a given e-mail. The experiment results show that our approach is promising with an average accuracy of 82.2 . The identification of the authorship of e-mail messages is of increasing importance due to an increase in the use of e-mail for criminal purposes. An author’s unique writing style can be reduced to a pattern by making measurements of various stylometric features from the written text. This paper reports on work to optimize and extend an existing C# based stylometry system that identifies the author of an arbitrary e-mail by using fifty-five writing style features. The program has been extended to provide feature vector data in a format appropriate for distribution to other project teams for subsequent data mining and classification experiments. ABSTRACTWe propose various self-exciting point process models for the times when e-mails are sent between individuals in a social network. Using an expectation–maximization (EM)-type approach, we fit these models to an e-mail network dataset from West Point Military Academy and the Enron e-mail dataset. We argue that the self-exciting models adequately capture major temporal clustering features in the data and perform better than traditional stationary Poisson models. We also investigate how accounting for diurnal and weekly trends in e-mail activity improves the overall fit to the observed network data. A motivation and application for fitting these self-exciting models is to use parameter estimates to characterize important e-mail communication behaviors such as the baseline sending rates, average reply rates, and average response times. A primary goal is to use these features, estimated from the self-exciting models, to infer the underlying leadership status of users in the West Point and Enron network... The "conversion rate" of spam--the probability that an unsolicited e-mail will ultimately elicit a "sale"--underlies the entire spam value proposition. However, our understanding of this critical behavior is quite limited, and the literature lacks any quantitative study concerning its true value. In this paper we present a methodology for measuring the conversion rate of spam. Using a parasitic infiltration of an existing botnet's infrastructure, we analyze two spam campaigns: one designed to propagate a malware Trojan, the other marketing on-line pharmaceuticals. For nearly a half billion spam e-mails we identify the number that are successfully delivered, the number that pass through popular anti-spam filters, the number that elicit user visits to the advertised sites, and the number of "sales" and "infections" produced. TLS is the most widely used cryptographic protocol on the Internet today. While multiple recent studies focused on its use in HTTPS and the adoption rate of additional security measures over time, the usage of TLS in e-mail-related protocols is still lacking detailed insights. End-to-end encryption mechanisms like PGP are seldomly used, and as such today's confidentiality in the e-mail ecosystem is based entirely on the encryption of the transport layer. However, a large fraction of e-mails is still transmitted unencrypted, which is highly disproportionate with the sensitive nature of e-mail communication content. A well-positioned attacker may be able to intercept plaintext communication content as well as communication metadata passively and at ease. We are the first to collect and analyze the complete state of today's e-mail-related TLS configuration, for the entire IPv4 address range. Our methodology is based on commodity hardware and open-source software, and we draw a comprehensive picture of the current state of security mechanisms on the transport layer for e-mail by scanning cipher suite support which was previously considered impossible due to numerous constraints. We collected and scanned a massive dataset of 20 million IP port combinations of all e-mail-related protocols (SMTP, POP3, IMAP). Over a time span of approx. Three months we conducted more than 10 billion TLS handshakes. Additionally, we show that securing server-to-server communication using e.g. SMTP is inherently more difficult than securing client-to-server communication, and that while the overall trend points in the right direction there are still many steps needed towards secure e-mail. Historically, mailing lists have been the preferred means for coordinating development and user support activities. With the emergence and popularity growth of social Q&A sites such as the StackExchange network (e.g., StackOverflow), this is beginning to change. Such sites offer different socio-technical incentives to their participants than mailing lists do, e.g., rich web environments to store and manage content collaboratively, or a place to showcase their knowledge and expertise more vividly to peers or potential recruiters. A key difference between StackExchange and mailing lists is gamification, i.e., StackExchange participants compete to obtain reputation points and badges. In this paper, we use a case study of R (a widely-used tool for data analysis) to investigate how mailing list participation has evolved since the launch of StackExchange. Our main contribution is the assembly of a joint data set from the two sources, in which participants in both the texttt r-help mailing list and StackExchange are identifiable. This permits their activities to be linked across the two resources and also over time. With this data set we found that user support activities show a strong shift away from texttt r-help . In particular, mailing list experts are migrating to StackExchange, where their behaviour is different. First, participants active both on texttt r-help and on StackExchange are more active than those who focus exclusively on only one of the two. Second, they provide faster answers on StackExchange than on texttt r-help , suggesting they are motivated by the emph gamified environment. To our knowledge, our study is the first to directly chart the changes in behaviour of specific contributors as they migrate into gamified environments, and has important implications for knowledge management in software engineering. Understanding what interests and delights users is critical to effective behavioral targeting, especially in information-poor contexts. As users interact with content and advertising, their passive behavior can reveal their interests towards advertising. Two issues are critical for building effective targeting methods: what metric to optimize for and how to optimize. More specifically, we first attempt to understand what the learning objective should be for behavioral targeting so as to maximize advertiser's performance. While most popular advertising methods optimize for user clicks, as we will show, maximizing clicks does not necessarily imply maximizing purchase activities or transactions, called conversions, which directly translate to advertiser's revenue. In this work we focus on conversions which makes a more relevant metric but also the more challenging one. Second is the issue of how to represent and combine the plethora of user activities such as search queries, page views, ad clicks to perform the targeting. We investigate several sources of user activities as well as methods for inferring conversion likelihood given the activities. We also explore the role played by the temporal aspect of user activities for targeting, e.g., how recent activities compare to the old ones. Based on a rigorous offline empirical evaluation over 200 individual advertising campaigns, we arrive at what we believe are best practices for behavioral targeting. We deploy our approach over live user traffic to demonstrate its superiority over existing state-of-the-art targeting methods. In a database to which data is continually added, users may wish to issue a permanent query and be notified whenever data matches the query. If such continuous queries examine only single records, this can be implemented by examining each record as it arrives. This is very efficient because only the incoming record needs to be scanned. This simple approach does not work for queries involving joins or time. The Tapestry system allows users to issue such queries over a database of mail and bulletin board messages. The user issues a static query, such as “show me all messages that have been replied to by Jones,” as though the database were fixed and unchanging. Tapestry converts the query into an incremental query that efficiently finds new matches to the original query as new messages are added to the database. This paper describes the techniques used in Tapestry, which do not depend on triggers and thus be implemented on any commercial database that supports SQL. Although Tapestry is designed for filtering mail and news messages, its techniques are applicable to any append-only database. Current Internet e-mail facilities are built onto the foundation of standard rules and protocols, which usually allow a considerable amount of ''freedom'' to their designers. Each of these standards has been defined based on a number of vendor specific implementations, in order to provide common inter-working procedures for cross-vendor communication. Thus, a lot of optional and redundant information is being exchanged during e-mail sessions, which is available to implement versatile covert channel mechanisms. This work exploits this possibility by presenting a simple but effective steganographic scheme that can be used to deploy robust secret communication through spam e-mails. This scheme can offer unidirectional asynchronous one-to-one or one-to-many covert channel facilities that are able to bypass the most sophisticated firewalls and traffic analyzers. Its implementation neither affects the involved transport protocols nor causes any perceivable performance degradation or data loss to the end-users. The proposed scheme allows one to manage possible filtering loss of the e-mails being the vehicle of the secret information. A novel retransmission method based on the Raptor codes has been adopted. The use of Raptor codes is key to correctly and efficiently manage the difficulty or impossibility to retransmit e-mails in the case of a unidirectional secret communication starting from one sender and directed to many recipients. In order to evaluate the performance characteristics of the proposed scheme, an empirical estimation of the covert channel bandwidth has been performed. Recently, social networks such as Facebook have experienced a huge surge in popularity. The amount of personal information stored on these sites calls for appropriate security precautions to protect this data. In this paper, we describe how we are able to take advantage of a common weakness, namely the fact that an attacker can query popular social networks for registered e-mail addresses on a large scale. Starting with a list of about 10.4 million email addresses, we were able to automatically identify more than 1.2 million user profiles associated with these addresses. By automatically crawling and correlating these profiles, we collect detailed personal information about each user, which we use for automated profiling (i.e., to enrich the information available from each user). Having access to such information would allow an attacker to launch sophisticated, targeted attacks, or to improve the efficiency of spam campaigns. We have contacted the most popular providers, who acknowledged the threat and are currently implementing our proposed countermeasures. Facebook and XING, in particular, have recently fixed the problem.