Clickstream data is an important source to enhance user experience and pursue business objectives in e-commerce. The paper uses clickstream data to predict online shopping behavior and target marketing interventions in real-time. Such AI-driven targeting has proven to save huge amounts of marketing costs and raise shop revenue. Previous user behavior prediction models rely on supervised machine learning (SML). Conceptually, SML is less suitable because it cannot account for the sequential structure of clickstream data. The paper proposes a methodology capable of unlocking the full potential of clickstream data using the framework of recurrent neural networks (RNNs). An empirical evaluation based on real-world e-commerce data systematically assesses multiple RNN classifiers and compares them to SML benchmarks. To this end, the paper proposes an approach to measure the revenue impact of a targeting model. Estimates of revenue impact together with results of standard classifier performance metrics evidence the viability of RNN-based clickstream modeling and guide employing deep recurrent learners for campaign targeting. Given that the empirical analysis shows RNN-based and conventional classifiers to capture different patterns in clickstream data, a specific recommendation is to combine sequence and conventional classifiers in an ensemble. The paper shows such an ensemble to consistently outperform the alternative models considered in the study.

This paper describes a novel approach to product recommendation that is based on opinionated product descriptions that are automatically mined from user-generated product reviews. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers. We demonstrate the benefits of this approach across a variety of Amazon product domains.

In this paper we consider the application of content-based recommendation techniques to web curation services which allow users to curate and share topical collections of content (e.g. images, news, web pages etc.). Curation services like Pinterest are now a mainstay of the modern web and present a range of interesting recommendation challenges. In this paper we consider the task of recommending collections to users and evaluate a range of different content-based techniques across a variety of content signals. We present the results of a large-scale evaluation using data from the Scoop.it web page curation service

User opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. Indeed this has become a vital part of the service provided by sites like Amazon and TripAdvisor, so much so that many of us will routinely check appropriate product reviews before making a purchase decision, regardless of whether we intend to purchase online or not. The importance of reviews has highlighted the need to help users to produce better reviews and in this paper we describe the development and evaluation of a Reviewer's Assistant for this purpose. We describe a browser plugin that is designed to work with major sites like Amazon and to provide users with suggestions as they write their reviews. These suggestions take the form of topics (e.g. product features) that a reviewer may wish to write about and the suggestions automatically adapt as the user writes their review. We describe and evaluate a number of different algorithms to identify useful topics to recommend to the user and go on to describe the results of a preliminary live-user trial.

User opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. The importance of reviews has driven the need to improve the review quality by providing interactive support for the reviewer and we will demonstrate the first version of an Intelligent Reviewer's Assistant for this purpose. Our browser plugin is designed to work with major sites like Amazon and to provide users with suggestions as they write their reviews. In particular, these suggestions take the form of topics (e.g. product features) that a reviewer may wish to write about and the suggestions automatically adapt as the user writes their review.

User generated content has become a valuable part of the Social Web, and high quality web content is used to influence buying decisions in a growing number of business domains. Numerous information quality frameworks have been proposed in the last years. Since 1989 at least twenty frameworks were written concerning information quality in different contexts. Most of these frameworks have been developed for databases or data warehousing in a business context. But information quality has also gained vital importance for collaboratively created content in the so-called Web 2.0. Based on literature reviews, recent publications, and Grounded Theory research on Web 2.0 sites, we propose an extended set and a new classification of information quality dimensions for the Social Web in general. Our comprehensive list of information quality dimensions can be applied to the engineering, analysis, and management of digital ecosystems and serves as a base for future research in this area.

User generated reviews are now a familiar and valuable part of most e-commerce sites since high quality reviews are known to influence purchasing decisions. In this demonstration we describe work on the Reviewer's Assistant (RA), which is a recommendation system that is designed to help users to write better quality reviews. It does this by suggesting relevant topics that they may wish to discuss based on the product they are reviewing and the content of their review so far.

With the emergence of Web 2.0 applications, where information is not only shared across the internet, but also syndicated, evaluated, selected, recombined, edited, etc., quality emergence by collaborative effort from many users becomes crucial. However, users may have low expertise, subjective views, or competitive goals. Therefore, we need to identify cooperative users with strong expertise and high objectivity.

It is well-known that commitment is an important ingredient for contributions of high quality. With the Internet heading towards being an actionable social space, rather than a collection of Web-sites, the issue of quality becomes crucial for sharing knowledge and action in collaborative scenarios. We propose a case study for the investigation of the influence of time, user feedback and interface complexity on contribution quality. We plan to exploit different online user groups across two different temporal phases for the evaluation.

We consider an active information system, which aims to notify a traveler timely about a likely delay. Besides providing the right content, it should also wait for the best time for notification. This is especially true for mobile information services, where every notification may irritate the recipient. We formulate and solve such a problem involving best choices both for content and time of notification by modeling it as an influence diagram. The chosen example stems from the domain of personal travel assistance. Note that the action of the traveler is nothing else than a reaction to the notification from the information system.