A Context-Aware Mobile Recommender System for Places of Interest

In this paper we introduce a novel setting mindful portable recommender framework for spots of intrigue (POIs). Not at all like existing frameworks, which gain clients' inclinations exclusively from their past evaluations, has it considered additionally their identity - utilizing the Five Factor Model. Identity is gained by requesting that clients finish a brief and engaging poll as a major aspect of the enlistment procedure, and is then misused in: (1) a dynamic learning module that effectively obtains evaluations in-setting for POIs that clients are probably going to have encountered, consequently diminishing the anxiety and inconvenience to rate (or skip rating) things that the clients don't have a clue; and (2) in the suggestion display that develops on network factorization and in this manner can be prepared regardless of the possibility that the clients haven't appraised any things yet

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

Friends don't lie: inferring personality traits from social network structure

In this work, we investigate the relationships between social network structure and personality; we assess the performances of different subsets of structural network features, and in particular those concerned with ego-networks, in predicting the Big-5 personality traits. In addition to traditional survey-based data, this work focuses on social networks derived from real-life data gathered through smartphones. Besides showing that the latter are superior to the former for the task at hand, our results provide a fine-grained analysis of the contribution the various feature sets are able to provide to personality classification, along with an assessment of the relative merits of the various networks exploited.European Commission (PERSI Project within the Marie Curie COFUND-FP7)Italy. Ministero dell'istruzione, dell'università e della ricerca (FIRB S-PATTERNS project

From Dataveillance to Datapulation : The Dark Side of Targeted Persuasive Technologies

Online services, devices or secret services are constantly collecting data and meta-data from their users. This data collection is mostly used to target users, customized their services or monitor them. This surveillance by the data, sometimes referred to as Dataveillance, is omnipresent and generates a lot of attention [6]. However nowadays, data and technologies are not only used to monitor people, they are also used to motivate, influence or shape their opinions or decisions online. The better understanding of users' behaviors combined with the capacity of building accurate psychological profiles create the opportunities to develop techniques to influence users online, by shaping their behavior. These technologies can encourage positive norms, such as fighting terrorist or racists propaganda online, or can be used to motivate users to drive more safely or economically, to eat healthier or to exercise more 1. In this case, they are often referred to as "Persuasive technologies or profiling" by psychologists, designers or behavioral economists [11,3]. However, these "persuasive technologies" have also a dark side. They can constitute efficient and targeted informational weapons to deceive or manipulate users' opinions or behaviors maliciously, via fakes news, information disorder, psychological or media manipulation techniques [27]. We define, in the paper, the concept of Datapulation, manipulation by the data. Datapulation consists of "mediated" personalized manipulation techniques, based on information, created primarily to change the attitudes and behaviors of users, for malicious intends or intends that go against users' own interests. Datapulation can be used by commercial companies to increase profit [7,4] or by political parties to influence elections [15,8]. We argue that Datapulation can be dangerous for privacy, human rights and democracy, and deserves more attention by policy makers and researchers. The main goal of this paper is to define the concept of Datapulation, by formalizing how data can be used to manipulate our decisions. We believe this is an important step in order to address it properly

Computational personality recognition in social media

A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? (3) What is the decay in accuracy when porting models trained in one social media environment to another