Image collection summarization techniques aim to present a compact representation of an image gallery through a carefully selected subset of images that captures its semantic content. When it comes to web content, however, the ideal selection can vary based on the user's specific intentions and preferences. This is particularly relevant at Booking.com, where presenting properties and their visual summaries that align with users' expectations is crucial. To address this challenge, we consider user intentions in the summarization of property visuals by analyzing property reviews and extracting the most significant aspects mentioned by users. By incorporating the insights from reviews in our visual summaries, we enhance the summaries by presenting the relevant content to a user. Moreover, we achieve it without the need for costly annotations. Our experiments, including human perceptual studies, demonstrate the superiority of our cross-modal approach, which we coin as CrossSummarizer over the no-personalization and image-based clustering baselines.

In online experimentation, appropriate metrics (e.g., purchase) provide strong evidence to support hypotheses and enhance the decision-making process. However, incomplete metrics are frequently occurred in the online experimentation, making the available data to be much fewer than the planned online experiments (e.g., A/B testing). In this work, we introduce the concept of dropout buyers and categorize users with incomplete metric values into two groups: visitors and dropout buyers. For the analysis of incomplete metrics, we propose a clustering-based imputation method using $k$-nearest neighbors. Our proposed imputation method considers both the experiment-specific features and users' activities along their shopping paths, allowing different imputation values for different users. To facilitate efficient imputation of large-scale data sets in online experimentation, the proposed method uses a combination of stratification and clustering. The performance of the proposed method is compared to several conventional methods in both simulation studies and a real online experiment at eBay.

New York: Dynamic Yield, the AI-Powered Personalization Anywhere platform, today announced the release of Affinity-Based Personalization – a new capability allowing brands to leverage automatically generated user affinity profiles to easily build a new class of user segments and deliver highly-targeted experiences across channels. While retailers track purchases, they've missed out on a trove of highly valuable information in the form of product and category browsing activity over the years. This structured data can be used for the creation of more relevant and individualized marketing campaigns, but instead, often goes completely unsaved. Dynamic Yield can now match a visitor's browsing activities with the attributes of the products they interact with, leveraging the data to build a unique affinity profile for each individual in real-time. In turn, this allows for the automated creation of new and sophisticated user segments that can be utilized across all channels, including web, mobile, app, email, as well as sent to external third party platforms like DMPs, BI tools, DSPs and more.

Web services are basic functions of a software system to support the concept of service-oriented architecture. They are often composed together to provide added values, known as web service composition. Researchers often employ Evolutionary Computation techniques to efficiently construct composite services with near-optimized functional quality (i.e., Quality of Semantic Matchmaking) or non-functional quality (i.e., Quality of Service) or both due to the complexity of this problem. With a significant increase in service composition requests, many composition requests have similar input and output requirements but may vary due to different preferences from different user segments. This problem is often treated as a multi-objective service composition so as to cope with different preferences from different user segments simultaneously. Without taking a multi-objective approach that gives rise to a solution selection challenge, we perceive multiple similar service composition requests as jointly forming an evolutionary multi-tasking problem in this work. We propose an effective permutation-based evolutionary multi-tasking approach that can simultaneously generate a set of solutions, with one for each service request. We also introduce a neighborhood structure over multiple tasks to allow newly evolved solutions to be evaluated on related tasks. Our proposed method can perform better at the cost of only a fraction of time, compared to one state-of-art single-tasking EC-based method. We also found that the use of the proper neighborhood structure can enhance the effectiveness of our approach.

As a digital analyst or marketer, you know the importance of analytical decision making. Go to any industry conference, blog, meet up, or even just read the popular press, and you will hear and see topics like machine learning, artificial intelligence, and predictive analytics everywhere. Because many of us don't come from a technical/statistical background, this can be both a little confusing and intimidating. But don't sweat it, in this post, I will try to clear up a some of this confusion by introducing a simple, yet powerful framework – the intelligent agent – which will help link these new ideas with familiar tools and concepts like A/B Testing and Optimization. Note: the intelligent agent framework is used as the guiding principle in Russell and Norvig's excellent text Artificial Intelligence: A Modern Approach – it's an awesome book, and I recommend anyone who wants to learn more to go get a copy or check out their online AI course.

The first step is mapping the users in our database to identify raw features that are most related to user quality. Features can be activity of users in similar apps, IAP purchase activity, etc. This data allow us to create different user segments. For example, active users can be put into active and very active user segments while spenders can be put into low, average, and high spender segments. If we want to target only very active users and high spenders, the created segments can be utilized to find audiences who share similar traits and conduct similar behaviors as the selected segments.

Posted by Lance Olson, Partner Director of Program Management in the Data Group at Microsoft. Cortana Intelligence with Bing Predicts preview is an end-to-end consulting program that brings the power of Microsoft's unique corpus of social, search and web data to let customers enrich and augment their Cortana Intelligence Suite solutions resulting in more accurate outcomes across a wide variety of business problems. The program springs from the highly successful and well-regarded Bing Predicts consumer experience, where Bing correctly predicted every knockout game of the 2014 soccer World Cup and 95% of the 2014 US mid-term elections. This program was born when we saw the opportunity to take the unique data assets that we have from our consumer businesses and help our commercial customers. Our team interprets these datasets to help you gain unique insights such as localized consumer preferences, user sentiment, customer demographics, macroeconomic indicators predictions and industry trends.

Machine Learning is now on top of the Gartner hype cycle, and almost everyone in the world of digital marketing or e-Commerce talks about it or, at least, has heard about it. But although this term is discussed quite often, only a few do really know what it is or what it can do for them. Even less are aware about its limits. Within this article, we want to shed light on this dark through introducing you to the concept of machine learning, showing its application in online shops and comparing it to Operational Intelligence and Prescriptive Analysis for a better understanding. Basically, Machine Learning is a part of Artificial Intelligence (AI) that generates knowledge from experiences, i.e. historical data.

We describe a new content publishing system that selects articles to serve to a user, choosing from an editorially programmed pool that is frequently refreshed. It is now deployed on a major Internet portal, and selects articles to serve to hundreds of millions of user visits per day, significantly increasing the number of user clicks over the original manual approach, in which editors periodically selected articles to display. Some of the challenges we face include a dynamic content pool, short article lifetimes, non-stationary click-through rates, and extremely high traffic volumes. The fundamental problem we must solve is to quickly identify which items are popular(perhaps within different user segments), and to exploit them while they remain current. We must also explore the underlying pool constantly to identify promising alternatives, quickly discarding poor performers. Our approach is based on tracking per article performance in near real time through online models. We describe the characteristics and constraints of our application setting, discuss our design choices, and show the importance and effectiveness of coupling online models with a simple randomization procedure. We discuss the challenges encountered in a production online content-publishing environment and highlight issues that deserve careful attention. Our analysis of this application also suggests a number of future research avenues.