E-commerce web applications are almost ubiquitous in our day to day life, however as useful as they are, most of them have little to no adaptation to user needs, which in turn can cause both lower conversion rates as well as unsatisfied customers. We propose a machine learning system which learns the user behaviour from multiple previous sessions and predicts useful metrics for the current session. In turn, these metrics can be used by the applications to customize and better target the customer, which can mean anything from offering better offers of specific products, targeted notifications or placing smart ads. The data used for the learning algorithm is extracted from Google Analytics Enhanced E-commerce, which is enabled by most e-commerce websites and thus the system can be used by any such merchant. In order to learn the user patterns, only its behaviour features were used, which don't include names, gender or any other personal information that could identify the user. The learning model that was used is a double recurrent neural network which learns both intra-session and inter-session features. The model predicts for each session a probability score for each of the defined target classes.
Using Google Analytics data for Machine Learning purposes is a relatively new and rarely used method, perhaps because the analytics platform's original focus is on aggregated data. The User Explorer https: support.google.com analytics answer 6339208?hl=en report, which provides insights at the user level, is relatively new. In @cite_5 , they predict the amount of people visiting a city based on website traffic of various touristic websites. In @cite_1 , they try to identify the demography of the users on a website, studying the evolution of location as well as device used for some time frames. In @cite_10 , they analyzed the website traffic to sort the most visited pages for some online library. Then, based on the traffic analysis, they put more emphasis on the most visited ones, promoting them to the main page more often which resulted in a decrease of the bounce rate by a large margin. In @cite_0 , they analyze the performance of various e-commerce sites using computed statistics of the features offered by Google Analytics.
@cite_0 @cite_5 @cite_1 @cite_10
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Performance measurement of tourism websites is becoming a critical issue for effective online marketing. The aim of this article is to analyse the effectiveness of entries (visit behaviour and length of sessions) depending on their traffic source: direct visit, in-link entries (for instance, en.wikipedia.org), and search engine visits (for example, Google). For this purpose, time series analysis of Google Analytics data is made use of. This method could be interesting for any tourism website optimizer. The ability of 10 Google Analytics website traffic indicators from the Viennese DMO website to predict actual tourist arrivals to Vienna is investigated within the VAR model class. To prevent overparameterization, big data shrinkage methods are applied: Bayesian estimation of the VAR, reduction to a factor-augmented VAR, and application of Bayesian estimation to the FAVAR, the novel Bayesian FAVAR. Forecast accuracy results show that for shorter horizons (h=1, 2months ahead) a univariate benchmark performs best, while for longer horizons (h=3, 6, 12) forecast combination methods that include the predictive information of Google Analytics perform best, notably combined forecasts based on Bates–Granger weights, on forecast encompassing tests, and on a novel fusion of these two. To partly address people's concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google's ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user's profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies. Online advertising drives the economy of the World Wide Web. Modern websites of any size and popularity include advertisements to monetize visits from their users. To this end, they assign an area of their web page to an advertising company (so called ad exchange) that will use it to display promotional content. By doing this, the website owner implicitly trusts that the advertising company will offer legitimate content and it will not put the site's visitors at risk of falling victims of malware campaigns and other scams. In this paper, we perform the first large-scale study of the safety of the advertisements that are encountered by the users on the Web. In particular, we analyze to what extent users are exposed to malicious content through advertisements, and investigate what are the sources of this malicious content. Additionally, we show that some ad exchanges are more prone to serving malicious advertisements than others, probably due to their deficient filtering mechanisms. The observations that we make in this paper shed light on a little studied, yet important, aspect of advertisement networks, and can help both advertisement networks and website owners in securing their web pages and in keeping their visitors safe. Background: The authors present a case study of a public health campaign, including social media, and aiming at maximizing the use of web app on sexual health. Objective: To analyze the impact of a Facebook fan page, Facebook advertisements, and posters to maximize the number of visits to the educational web app. Methods: The campaign is assessed for 1 year, using data tracked through Facebook statistics and Google Analytics. Results: The site had 3670 visits 10.1 visitors day, 95 CI 8.7-11.4. During the one-month Facebook Ads campaign, the site received 1263 visits 42.1 visitors day, 95 CI 37.3-46.9, multiplying by over four the average number of visitors day. 34.4 of all the participants were recruited during the one-month Facebook ads campaign. Conclusions: Facebook advertisements seem to be a good tool to promote an educational web app on sexual health targeting youth, and to reach a huge number of users rapidly and at a low cost. Online news reading has become very popular as the web provides access to news articles from millions of sources around the world. A key challenge of news websites is to help users find the articles that are interesting to read. In this paper, we present our research on developing personalized news recommendation system in Google News. For users who are logged in and have explicitly enabled web history, the recommendation system builds profiles of users' news interests based on their past click behavior. To understand how users' news interests change over time, we first conducted a large-scale analysis of anonymized Google News users click logs. Based on the log analysis, we developed a Bayesian framework for predicting users' current news interests from the activities of that particular user and the news trends demonstrated in the activity of all users. We combine the content-based recommendation mechanism which uses learned user profiles with an existing collaborative filtering mechanism to generate personalized news recommendations. The hybrid recommender system was deployed in Google News. Experiments on the live traffic of Google News website demonstrated that the hybrid method improves the quality of news recommendation and increases traffic to the site. ABSTRACTThe primary purpose of an academic library Web site is to serve as a portal to library-acquired content. Navigational design of a library Web site affects the user's ability to find and access content. At Albertsons Library, the goal of the navigational design of the Web site is to mimic user behavior on the Web site to help them access information and articles from over 300 different library vendors. Coordinating with different vendors makes tracking the navigational flow of user behavior difficult with the tool Google Analytics. Using the events feature in Google Analytics, the team responsible for Web design was able to track user flow, and was able to quantify how many users were actual “drop-offs” versus those that were clicks into library resources. Decisions made after acquiring these data resulted in a Web site with a 10 percent or less bounce rate, and decreased the number of clicks required for users accessing the library's content. The Internet gives individuals more choice in political news and information sources and more tools to filter out disagreeable information. Citing the preference described by selective exposure theory — people prefer information that supports their beliefs and avoid counter-attitudinal information — observers warn that people may use these tools to access only agreeable information and thus live in ideological echo chambers. We report on a field deployment of a browser extension that showed users feedback about the political lean of their weekly and all time reading behaviors. Compared to a control group, showing feedback led to a modest move toward balanced exposure, corresponding to 1-2 visits per week to ideologically opposing sites or 5-10 additional visits per week to centrist sites. Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' or non-immediate friends' pages. In summary, our analysis demonstrates the power of using clickstream data in identifying patterns in social network workloads and social interactions. Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92 of all user activities. Consequently, compared to using only crawled data, considering silent interactions like browsing friends' pages increases the measured level of interaction among users. Over the years, the web has evolved from simple text content from one server to a complex ecosystem with different types of content from servers spread across several administrative domains. There is anecdotal evidence of users being frustrated with high page load times or when obscure scripts cause their browser windows to freeze. Because page load times are known to directly impact user satisfaction, providers would like to understand if and how the complexity of their websites affects the user experience. While there is an extensive literature on measuring web graphs, website popularity, and the nature of web traffic, there has been little work in understanding how complex individual websites are, and how this complexity impacts the clients' experience. This paper is a first step to address this gap. To this end, we identify a set of metrics to characterize the complexity of websites both at a content-level (e.g., number and size of images) and service-level (e.g., number of servers origins). We find that the distributions of these metrics are largely independent of a website's popularity rank. However, some categories (e.g., News) are more complex than others. More than 60 of websites have content from at least 5 non-origin sources and these contribute more than 35 of the bytes downloaded. In addition, we analyze which metrics are most critical for predicting page render and load times and find that the number of objects requested is the most important factor. With respect to variability in load times, however, we find that the number of servers is the best indicator. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] Understanding how users navigate and interact when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present an in-depth analysis of user workloads in online social networks. This study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we gather the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' and non-immediate friends' pages. Results show that browsing, which cannot be inferred from crawling publicly available data, accounts for 92 of all user activities. Consequently, compared to using only crawled data, silent interactions like browsing friends' pages increase the measured level of interaction among users. Additionally, we find that friends requesting content are often within close geographical proximity of the uploader. We also discuss a series of implications of our findings for efficient system and interface design as well as for advertisement placement in online social networks. "Witkeys" are websites in China that form a rapidly growing web-based knowledge market. A user who posts a task also offers a small fee, and many other users submit their answers to compete. The Witkey sites fall in-between aspects of the now-defunct Google Answers (vetted experts answer questions for a fee) and Yahoo Answers (anyone can answer or ask a question). As such, these sites promise new possibilities for knowledge-sharing online communities, perhaps fostering the freelance marketplace of the future. In this paper, we investigate one of the biggest Witkey websites in China, Taskcn.com. In particular, we apply social network prestige measures to a novel construction of user and task networks based on competitive outcomes to discover the underlying properties of both users and tasks. Our results demonstrate the power of this approach: Our analysis allows us to infer relative expertise of the users and provides an understanding of the participation structure in Taskcn. The results suggest challenges and opportunities for this kind of knowledge sharing medium. To partly address people’s concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google’s ads, and Ad Settings interact. Our tool uses a rigorous experimental design and analysis to ensure the statistical significance of our results. It uses machine learning to automate the selection of a statistical test. We use AdFisher to find that Ad Settings is opaque about some features of a user’s profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse will change the ads shown but not the settings page. We also found that setting the gender to female results in getting fewer instances of an ad related to high paying jobs than setting it to male. More and more websites embed structured data describing for instance products, reviews, blog posts, people, organizations, events, and cooking recipes into their HTML pages using markup standards such as Microformats, Microdata and RDFa. This development has accelerated in the last two years as major Web companies, such as Google, Facebook, Yahoo!, and Microsoft, have started to use the embedded data within their applications. In this paper, we analyze the adoption of RDFa, Microdata, and Microformats across the Web. Our study is based on a large public Web crawl dating from early 2012 and consisting of 3 billion HTML pages which originate from over 40 million websites. The analysis reveals the deployment of the different markup standards, the main topical areas of the published data as well as the different vocabularies that are used within each topical area to represent data. What distinguishes our work from earlier studies, published by the large Web companies, is that the analyzed crawl as well as the extracted data are publicly available. This allows our findings to be verified and to be used as starting points for further domain-specific investigations as well as for focused information extraction endeavors. The Internet has evolved into a huge video delivery infrastructure, with websites such as YouTube and Netflix appearing at the top of most traffic measurement studies. However, most traffic studies have largely kept silent about an area of the Internet that (even today) is poorly understood: adult media distribution. Whereas ten years ago, such services were provided primarily via peer-to-peer file sharing and bespoke websites, recently these have converged towards what is known as Porn 2.0''. These popular web portals allow users to upload, view, rate and comment videos for free. Despite this, we still lack even a basic understanding of how users interact with these services. This paper seeks to address this gap by performing the first large-scale measurement study of one of the most popular Porn 2.0 websites: YouPorn. We have repeatedly crawled the website to collect statistics about 183k videos, witnessing over 60 billion views. Through this, we offer the first characterisation of this type of corpus, highlighting the nature of YouPorn's repository. We also inspect the popularity of objects and how they relate to other features such as the categories to which they belong. We find evidence for a high level of flexibility in the interests of its user base, manifested in the extremely rapid decay of content popularity over time, as well as high susceptibility to browsing order. Using a small-scale user study, we validate some of our findings and explore the infrastructure design and management implications of our observations. This paper presents a study of the life cycle of news articles posted online. We describe the interplay between website visitation patterns and social media reactions to news content. We show that we can use this hybrid observation method to characterize distinct classes of articles. We also find that social media reactions can help predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from a large international news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions. Achieving the same prediction accuracy with visits alone would require to wait for three hours of data. We also describe significant improvements on the accuracy of the early prediction of shelf-life for news stories. In August 2009, we demonstrated that popular websites were using “Flash cookies” to track users. Some advertisers had adopted this technology because it allowed persistent tracking even where users had taken steps to avoid web profiling. We also demonstrated “respawning” on top sites with Flash technology. This allowed sites to reinstantiate HTTP cookies deleted by a user, making tracking more resistant to users’ privacy-seeking behaviors.In this followup study, we reassess the Flash cookies landscape and examine a new tracking vector, HTML5 local storage and Cache-Cookies via ETags. We found over 5,600 standard HTTP cookies on popular sites, over 4,900 were from third parties. Google-controlled cookies were present on 97 of the top 100 sites, including popular government websites. Seventeen sites were using HTML5, and seven of those sites had HTML5 local storage and HTTP cookies with matching values. Flash cookies were present on 37 of the top 100 sites. We found two sites that were respawning cookies, including one site – hulu.com – where both Flash and cache cookies were employed to make identifiers more persistent. The cache cookie method used ETags, and is capable of unique tracking even where all cookies are blocked by the user and “Private Browsing Mode” is enabled.Our 2009 study is also available at SSRN: http: ssrn.com abstract=1446862. Online advertising is an important and huge industry. Having knowledge of the website attributes can contribute greatly to business strategies for ad-targeting, content display, inventory purchase or revenue prediction. Classical inferences on users and sites impose challenge, because the data is voluminous, sparse, high-dimensional and noisy. In this paper, we introduce a stochastic blockmodeling for the website relations induced by the event of online user visitation. We propose two clustering algorithms to discover the instrinsic structures of websites, and compare the performance with a goodness-of-fit method and a deterministic graph partitioning method. We demonstrate the effectiveness of our algorithms on both simulation and AOL website dataset. Search sessions consist of a person presenting a query to a search engine, followed by that person examining the search results, selecting some of those search results for further review, possibly following some series of hyperlinks, and perhaps backtracking to previously viewed pages in the session. The series of pages selected for viewing in a search session, sometimes called the click data, is intuitively a source of relevance feedback information to the search engine. We are interested in how that relevance feedback can be used to improve the search results quality for all users, not just the current user. For example, the search engine could learn which documents are frequently visited when certain search queries are given. In this article, we address three issues related to using click data as implicit relevance feedback: (1) How click data beyond the search results page might be more reliable than just the clicks from the search results page; (2) Whether we can further subselect from this click data to get even more reliable relevance feedback; and (3) How the reliability of click data for relevance feedback changes when the goal becomes finding one document for the user that completely meets their information needs (if possible). We refer to these documents as the ones that are strictly relevant to the query. Our conclusions are based on empirical data from a live website with manual assessment of relevance. We found that considering all of the click data in a search session as relevance feedback has the potential to increase both precision and recall of the feedback data. We further found that, when the goal is identifying strictly relevant documents, that it could be useful to focus on last visited documents rather than all documents visited in a search session.