Averting the next credibility crisis in psychological science: Within-person methods for personalized diagnostics and intervention.

Personalizing assessments, predictions, and treatments of individuals is currently a defining trend in psychological research and applied fields, including personalized learning, personalized medicine, and personalized advertisement. For instance, the recent pandemic has reminded parents and educators of how challenging yet crucial it is to get the right learning task to the right student at the right time. Increasingly, psychologists and social scientists are realizing that the between- person methods that we have long relied upon to describe, predict, and treat individuals may fail to live up to these tasks (e.g., Molenaar, 2004). Consequently, there is a risk of a credibility loss, possibly similar to the one seen during the replicability crisis (Ioannides, 2005), because we have only started to understand how many of the conclusions that we tend to draw based on between-person methods are based on a misunderstanding of what these methods can tell us and what they cannot. An imminent methodological revolution will likely lead to a change of even well-established psychological theories (Barbot et al., 2020). Fortunately, methodological solutions for personalized descriptions and predictions, such as many within-person analyses, are available and undergo rapid development, although they are not yet embraced in all areas of psychology, and some come with their own limitations. This article first discusses the extent of the theory-method gap, consisting of theories about within-person patterns being studied with between-person methods in psychology, and the potential loss of trust that might follow from this theory-method gap. Second, this article addresses advantages and limitations of available within- person methods. Third, this article discusses how within-person methods may help improving the individual descriptions and predictions that are needed in many applied fields that aim for tailored individual solutions, including personalized learning and personalized medicine.

Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design Framework

This work conducts a systematic literature review about the domain of personalized advertisement, and more specifically, about the techniques that are used for this purpose. State-of-the-art publications and techniques are presented in detail, and the relationship of this domain with other related domains such as artificial intelligence (AI), semantic web, etc., is investigated. Important issues such as (a) business data utilization in personalized advertisement models, (b) the cold start problem in the domain, (c) advertisement visualization issues, (d) psychological factors in the personalization models, (e) the lack of rich datasets, and (f) user privacy are highlighted and are pinpointed to help and inspire researchers for future work. Finally, a design framework for personalized advertisement systems has been designed based on these findings.

Impact of Personalized Social Media Advertising on Online Impulse Buying Behavior

Personalization is used for marketing in social media by marketers and advertisers. So there is a great need to explore this phenomenon of personalization and online impulse buying behavior. This study examines the impact of personalized advertisement and its effects on online impulse buying behavior. This study is significant for online retailers and marketers. In this study, we developed a conceptual model. We then tested it while using different factors to know the power and impact of personalized advertisement on online impulse buying behavior through social media. We see perceived novelty and perceived relevance and online payment facility as mediators between personalized advertisement and online impulse buying behavior and privacy concerns as a moderator between payment facility and online impulse buying behavior. Developed a survey and filled it with 250 participants, then performed an analysis of correlation and regression; ten of the hypotheses of this study are supported by the finding of the results. And at the last chapter discussed the results and practical implications, and conclusion of the study.

Averting the next credibility crisis in Psychological Science: Within-person methods for personalized diagnostic and intervention

Personalizing assessments, predictions, and treatments of individuals is currently a defining trend in psychological research and applied fields, including personalized learning, personalized medicine, and personalized advertisement. For instance, the recent pandemic has reminded parents and educators of how challenging yet crucial it is to get the right learning task to the right student at the right time. Increasingly, psychologists and social scientists are realizing that the between-person methods that we have long been using in the hope to describe, predict, and treat individuals may fail to live up to these tasks (e.g., Molenaar, 2004). Consequently, there is a risk of a credibility loss, possibly similar to the one seen during the replicability crisis (Ioannides, 2005), because we have only started to understand how many of the conclusions that we tend to draw based on between-person methods misunderstand what these methods can tell us and what they cannot. An imminent methodological revolution will likely change even very established psychological theories (Barbot et al., 2020). Fortunately, methodological solutions for personalized descriptions and predictions, such as many within-person analyses, are available and rapidly being developed, although they are not yet embraced in all areas of Psychology, and some come with their own limitations. This article first discusses the extent of the theory-method gap between theories about within-person patterns versus methods examining only between-person patterns in Psychology, and the potential loss of trust that might follow from these limitations of the commonly used between-person methods. Second, this article addresses advantages and limitations of available within-person methods. Third, this article discusses how within-person analytical methods may help improving the individual descriptions and predictions that are needed in many applied fields aiming for tailored individual solutions, including personalized learning with educational technology and personalized medicine.

Deep Learning Based Real Age and Gender Estimation from Unconstrained Face Image towards Smart Store Customer Relationship Management

The COVID-19 pandemic markedly changed the human shopping nature, necessitating a contactless shopping system to curb the spread of the contagious disease efficiently. Consequently, a customer opts for a store where it is possible to avoid physical contacts and shorten the shopping process with extended services such as personalized product recommendations. Automatic age and gender estimation of a customer in a smart store strongly benefit the consumer by providing personalized advertisement and product recommendation; similarly, it aids the smart store proprietor to promote sales and develop an inventory perpetually for the future retail. In our paper, we propose a deep learning-founded enterprise solution for smart store customer relationship management (CRM), which allows us to predict the age and gender from a customer’s face image taken in an unconstrained environment to facilitate the smart store’s extended services, as it is expected for a modern venture. For the age estimation problem, we mitigate the data sparsity problem of the large public IMDB-WIKI dataset by image enhancement from another dataset and perform data augmentation as required. We handle our classification tasks utilizing an empirically leading pre-trained convolutional neural network (CNN), the VGG-16 network, and incorporate batch normalization. Especially, the age estimation task is posed as a deep classification problem followed by a multinomial logistic regression first-moment refinement. We validate our system for two standard benchmarks, one for each task, and demonstrate state-of-the-art performance for both real age and gender estimation.

Search Engine as a mediating technology of organization

Search engines have become the technological and organizational means to navigate, filter, and rank online information for users. During the seventeenth to nineteenth centuries in Europe, the ‘pre-history’ of search engines were the ‘bureau d’adresse’ or ‘address office’ that provided information and services to clients as they gathered data. Registers, censuses, and archives eventually shifted to relational databases owned by commercial platforms, advertising agencies cum search engines that provide non-neutral answers in exchange for user data. With ‘cyberorganization’, personalized advertisement, machine-learning algorithms, and ‘surveillance capitalism’ organize the user through their ‘habit’ of search. However, there are alternatives such as the p2p search engine YaCy and anonymity browsing with Tor.

4 Years of EU Cookie Law: Results and Lessons Learned

Personalized advertisement has changed the web. It lets websites monetize the content they offer. The downside is the continuous collection of personal information with significant threats to personal privacy. In 2002, the European Union (EU) introduced a first set of regulations on the use of online tracking technologies. It aimed, among other things, to make online tracking mechanisms explicit to increase privacy awareness among users. Amended in 2009, the EU Directive mandates websites to ask for informed consent before using any kind of profiling technology, e.g., cookies. Since 2013, the ePrivacy Directive became mandatory, and each EU Member State transposed it in national legislation. Since then, most of European websites embed a “Cookie Bar”, the most visible effect of the regulation. In this paper, we run a large-scale measurement campaign to check the current implementation status of the EU cookie directive. For this, we use CookieCheck, a simple tool to automatically verify legislation violations. Results depict a shady picture: 49 % of websites do not respect the Directive and install profiling cookies before any user’s consent is given. Beside presenting a detailed picture, this paper casts lights on the difficulty of legislator attempts to regulate the troubled marriage between ad-supported web services and their users. In this picture, online privacy seems to be continuously at stake, and it is hard to reach transparency.