Social networks have been applied in football, or football match analysis to analyze the passing distributions between teams. However, analysis has been mostly done on a manually collected data by considering the widely adopted network metrics such as betweenness and closeness centrality. In this paper, we use professional tracking event data provided by OPTA Sports and analyze the final game of the Euro2016 between Portugal and France. We use Gephi and the NetworkX Python library and apply dynamic network analysis by integrating the timestamps of the passes. We further look into traditional performance metrics from both teams and make an attempt to connect those to the network results and the outcome of the game.

Emotions have a significant impact on the purchasing process. Due to novel affective computing approaches, affective information of users can be acquired in implicit and therefore non-intrusive manner. Recent research in the field of recommender systems indicates that the incorporation of affective user information in the prediction model has a positive impact on the recommender systems accuracy. Existing research mainly focused on product recommendations in the movie anfd music domain. Our paper investigates the impact of affective emotions on fashion products, which is one of the largest consumer industries. We integrate the users' mood and their emotion in the prediction model, and the results are compared to the baseline model using rating data only. For this, we generate a dataset with 337 participants, 64 products, and 10816 ratings. We determine the mood information using the PANAS questionnaire, and the emotion by using the SAM self-assessment method. The affective information is integrated leveraging Factorization Machines. The evaluation of the offline experiments reveals that in new item cold-start scenarios the mood information has a positive impact on the prediction accuracy, whereas the emotion information has a negative impact.

Fashion product consumer are faced with large and fast changing product o erings. e fashion purchase decision process is complex, as the consumer has to consider various in uencing factors like current fashion trends, what fashion products t to their personality, and what products t to their physical appearance like hair colors or body measures. Based on novel technologies, 3D body avatars can be reconstructed from 3D or 2D data. From these avatars, body measures can be determined. e objective of this research is to investigate the predictive performance of body measures extracted from a 3D body scanner for predicting fashion item preferences. erefore, item preferences and body scans from 200 users were collected. From the body scans, 11 body measures are extracted and integrated into a prediction model using Factorization Machines. e results from a cross-validation show, that including body measurements signi cantly improves the prediction performance of the recommendation model, especially in new user scenarios, when no information about the fashion product preferences of the active user is known.

There are many platforms on the market that support researchers and practitioners to create surveys and market studies. Nevertheless, nearly all of them focus on providing answers to textual questions. In contrast to existing systems this paper presents the concept, prototype, and evaluation of a new mobile platform for quantitative research strictly focusing on images: the SciencOmat. This platform uses pictures to evaluate products, marketing content, and other elements based on their visual attractiveness. Particular emphasis was placed on a high level of usability and user experience. The system integrates methods known from popular online dating applications (e.g., liking/disliking a product by swiping left or right) and also applies gamification elements to further drive user motivation. Next to the application and its evaluation using the User Experience Questionnaire provided by Schrepp et al. we also present the results of two exemplary image data sets.

The performance of companies depends on the ability to leverage data to create insights and to target consumers with personalized messages Like marketing content or product offerings. One key element for personalized targeting are expressive user profiles, which are the basis for predictive models to estimate individual consumers’ preferences. Traditionally user profiles are mainly based on demographic attributes like age, gender, or occupation. Due to changes in society, consumers’ behaviors are less stable, and therefore these demographic factors are less effective. Alternatively, the consumers’ lifestyle has a significant impact on their purchase and consumption behavior. This paper investigates the relationship between Facebook Likes and the lifestyle of individuals based on the activity, interests, and opinion (AIO) model. Therefore, 14482 user-Like combinations from 214 participants were collected together with lifestyle information and a correlation analysis is conducted. The results indicate weak monotonic correlations between the AIO and the Like information.