Search

The current surge in the deployment of web applications underscores the need to consider users' individual preferences in order to enhance their experience. In response to this, an innovative approach is emerging that focuses on the detailed analysis of interaction data captured by web browsers. These data, which includes metrics such as the number of mouse clicks, keystrokes, and navigation patterns, offer insights into user behavior and preferences. By leveraging this information, developers can achieve a higher degree of personalization in web applications, particularly in the context of interactive elements such as online games. This paper presents the WebTraceSense project, which aims to pioneer this approach by developing a framework that encompasses a backend and frontend, advanced visualization modules, a DevOps cycle, and the integration of AI and statistical methods. The backend of this framework will be responsible for securely collecting, storing, and processing vast amounts of interaction data from various websites. The frontend will provide a user-friendly interface that allows developers to easily access and utilize the platform's capabilities. One of the key components of this framework is the visualization modules, which will enable developers to monitor, analyze, and interpret user interactions in real time, facilitating more informed decisions about user interface design and functionality. Furthermore, the WebTraceSense framework incorporates a DevOps cycle to ensure continuous integration and delivery, thereby promoting agile development practices and enhancing the overall efficiency of the development process. Moreover, the integration of AI methods and statistical techniques will be a cornerstone of this framework. By applying machine learning algorithms and statistical analysis, the platform will not only personalize user experiences based on historical interaction data but also infer new user behaviors and predict future preferences. In order to validate the proposed components, a case study was conducted which demonstrated the usefulness of the WebTraceSense framework in the creation of visualizations based on an existing dataset. [ABSTRACT FROM AUTHOR]

Learning path generation involves the computation of learning trajectories to personalize academic instruction to prevent school problems. The Educational Planning Problem ($EPP$ EPP) considers generating personalized learning paths by scheduling activities that satisfy expected grades while minimizing plans makespan. In this work, we propose two scheduling models with two new activity duration functions incorporating student stress and sequence dependence to mimic learning effects and increase personalization. We also developed a metaheuristic algorithm to solve real-size instances composed of a greedy randomized adaptive search procedure enhanced by a variable neighborhood search. Experimental results validate the efficiency of our approach and the importance of considering stress in learning trajectories personalization. [ABSTRACT FROM AUTHOR]

Background: Real‐world problems are important in math instruction, but they do not necessarily trigger students' task motivation. Personalizing real‐world problems by (1) matching problems to students' shared living environment (context personalization) and (2) asking students to pose their own problems (active personalization) might be two interventions to increase students' task motivation. Aim: In the current study, we investigated the effects of context personalization and active personalization on students' self‐efficacy expectations, intrinsic value, attainment value, utility value, and cost. Sample: The participants were 28 fifth‐ and sixth‐grade students who voluntarily took part in a six‐month afterschool program in which they posed problems with the aim of creating a math walk in their hometown. Method: Using a within‐subjects design, at the end of the afterschool program, the students rated their self‐efficacy expectations and task values for four self‐developed problems associated with their hometown, four peer‐developed problems associated with their hometown, and four instructor‐provided problems associated with unfamiliar locations. Results: Students reported higher self‐efficacy expectations, intrinsic value, attainment value, and utility value for active‐personalized than non‐personalized problems. To a lesser extent, context personalization promoted intrinsic value and attainment value. No effect was found for cost. Conclusions: Active personalization (i.e. asking students to pose their own real‐world problems) is suited to enhance students' task motivation, specifically their self‐efficacy expectations, intrinsic value, attainment value, and utility value. Context personalization still boosts students' intrinsic value and attainment value. Implementation in classroom instruction is discussed. [ABSTRACT FROM AUTHOR]

In the present days, culture has become a highly personal experience moving from its initial collective locus. As a consequence, the concept has undergone a process of unseen pluralization and expansion of meanings and interpretations. Herder is conventionally held responsible for coining the plural term ‘cultures’ by linking it with the eighteenth-century idea of nation. This conception evolved into a broader approach to look at cultural phenomena as national manifestations. Despite common assumptions, Herder did not ignore the role of the individual locus. Historical epistemology shows how, in the United States, a derivation of the Herderian notions took the pluralization of culture to the next level. By putting emphasis on the psychological and individual elements, the founders of American anthropology, Boas and his followers, revolutionized the conceptual frameworks in human and social sciences. It is possible to argue that personalization of culture is a secondary effect of Herder’s ideas. The Herderian-Boasian vision of culture mirrors the dichotomy between nationalization and personalization that activates strong conceptual mutations and reflects the dilemmas of the twenty-first century. [ABSTRACT FROM AUTHOR]

This study proposed a three-tier test to help students learn English grammar. To reduce students' anxiety, game-based learning was incorporated into the three-tier test, where personalization was also implemented to accommodate students' different needs. More specifically, we developed a Personalized Entertaining Three-Tier Test (PET3), which consisted of the three-tier test, game-based learning and personalization. On the other hand, diversities exist among learners, especially prior knowledge. To this end, this study aimed to provide the complete understandings of the effects of prior knowledge in the PET3 context, in terms of test performance, task performance, learning behavior and gaming behavior. Regarding test performance, high prior knowledge learners (HPK) performed better than low prior knowledge learners (LPK). Regarding task performance, HPK was superior to LPK in two aspects, i.e., the reasons of the main questions and advanced questions, while LPK performed better than HPK in the aspect of the review questions. Regarding learning behavior, HPK and LPK favored to use different hints. Regarding gaming behavior, HPK tended to be a winner while LPK had a tendency to be looser. Based on these findings, we developed a framework, which could help designers and instructors to know the preferences of diverse learners. [ABSTRACT FROM AUTHOR]

News reporting has long been seen as involving a form of storytelling but techniques for revealing narrative constructions in audiovisual news remain limited. As the forms of expression mobilised for news become ever more diverse and multimodal, the challenges posed for analysis grow accordingly. The present paper asks to what extent we can pursue more differentiating accounts of the increasingly diverse audiovisual features employed in news video by employing recent advances in multimodal discourse analysis. Specifically, we explore how to recognise storytelling strategies deployed in audiovisual news reporting both more reliably and in ways that complement conventional journalistic labels, such as those discussed in research into news sensationalism. As an illustration, we offer a contrastive analysis of the specific reporting strategies of 'individualisation' employed in 166 news reports from two leading German news channels, Tagesschau and Bild TV, produced between 1 January 2022 and 15 March 2022. We show how we scale analysis methods to achieve greater coverage of data and discuss how this meets several recurring challenges for multimodal analysis more generally. [ABSTRACT FROM AUTHOR]

Firms often employ personalized recommendations to help customers make purchase decisions. To improve the effectiveness of their personalized recommendations, some firms use cues to offer transparency on how they collect and use data to derive recommendations. We draw on attribution theory to propose an additional mechanism to improve the effectiveness of personalized recommendations with cues. Attributional cues, which refer to the underlying data (i.e., customers' own data vs. similar customers' data) used for personalized recommendations, aim to increase customers' self‐attribution of personalized recommendations. Specifically, in three experimental studies, we show that attributional cues increase customers' self‐attribution of personalized recommendations, leading to higher trust in and lower reactance to personalized recommendations. The accuracy and valence of the personalized recommendations moderate this attributional effect. As a result, employing attributional cues can be an essential and affordable tool for firms to increase the effectiveness of their personalized recommendations. [ABSTRACT FROM AUTHOR]

Information overload on e-commerce sites or applications makes users spend more resources than they should. Buyers should be able to use it according to their own habits, needs, wants and beliefs. Personalization of e-commerce is a solution that can be used to overcome this problem. In this paper, the researcher creates a framework for personalizing e-commerce, especially for small and medium enterprises. This framework consists of the customer behavior assumption, technology requirement and business assumption. The customer behavior assumption contains an approach to the habits/nature of the buyers themselves. By understanding this, e-commerce can offer something suitable and different for each person. Meanwhile, with technology requirements, e-commerce is able to adapt to the development of content and scalability of e-commerce itself. To make users feel comfortable and willing to go back to shopping at the same e-commerce, the business assumption is another component in this framework. We conclude that the use of this personalization framework can have a major impact on increasing user retention in e-commerce. [ABSTRACT FROM AUTHOR]

Personalization on digital platforms drives a broad range of harms, including misinformation, manipulation, social polarization, subversion of autonomy, and discrimination. In recent years, policy makers, civil society advocates, and researchers have proposed a wide range of interventions to address these challenges. This Article argues that the emerging toolkit reflects an individualistic view of both personal data and data-driven harms that will likely be inadequate to address growing harms in the global data ecosystem. It maintains that interventions must be grounded in an understanding of the fundamentally collective nature of data, wherein platforms leverage complex patterns of behaviors and characteristics observed across a large population to draw inferences and make predictions about individuals. Using the lens of the collective nature of data, this Article evaluates various approaches to addressing personalization-driven harms under current consideration. It also frames concrete guidance for future legislation in this space and for meaningful transparency that goes far beyond current transparency proposals. It offers a roadmap for what meaningful transparency must constitute: a collective perspective providing a third party with ongoing insight into the information gathered and observed about individuals and how it correlates with any personalized content they receive across a large, representative population. These insights would enable the third party to understand, identify, quantify, and address cases of personalization-driven harms. This Article discusses how such transparency can be achieved without sacrificing privacy and provides guidelines for legislation to support the development of such transparency. [ABSTRACT FROM AUTHOR]

With the rapid advancement of artificial intelligence (AI) technology, the advertising industry is at a crossroads of new opportunities and challenges. This pioneering study provides an in-depth review of AI's application in advertising, focusing especially on four key elements: Targeting, Personalization, Content Creation, and Ad Optimization. By delving deep into these areas, we uncover the potential of AI in revolutionizing the advertising sector. At the same time, we discuss the pressing ethical issues arising from the current applications of AI in advertising-related fields. Using the VOSviewer software, this study conducts an in-depth analysis of the literature, revealing the intrinsic connections of these four key elements in AI advertising based on computational advertising: Targeting and Personalization are closely linked, jointly determining who gets shown which advertisements. Content Creation generates appealing advertising content through AI during the Personalization process, while Ad Optimization relies on the outcomes of the first three elements, adjusting ad displays to achieve the highest return on investment. This research offers a fresh perspective on understanding AI's application in advertising, aiding in the responsible and effective use of AI technology for superior ad delivery. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has brought unprecedented challenges and opportunities for knowledge management systems (KMS) in various domains and sectors. This research explores the strategies to manage expectational knowledge in higher education after the COVID-19 pandemic. This study employed a qualitative research method with a case study design to capture the students' perspectives on strategies to manage expectational knowledge in higher education after the COVID-19 pandemic. The participants are students at the doctoral program, Universitas Pasundan, Bandung, West Java, Indonesia. The interview was conducted in December 2022 using a semi-structured interview technique and then was analyzed using thematic analysis. This research found four strategies doctoral management students use to manage expectation knowledge after the COVID-19 pandemic. The strategies are codification, personalization, knowledge partnering, and knowledge harvesting. The research implications of this study provide a foundation for future research endeavors to advance the understanding and application of knowledge management strategies in the post-pandemic higher education landscape. [ABSTRACT FROM AUTHOR]

A recommender system is an emerging personalization strategy in web applications to deal with information overload. Most recommender systems suggest items to users based on a single criterion, i.e., overall ratings. However, multi-criteria ratings are used for modeling more complex preferences of users. The incorporation of criteria ratings can lead to generating more reliable recommendations for users. Despite the success of multi-criteria methods, there is a need to be further optimized to handle popular issues, e.g., sparsity and cold-start. This research study presents a novel multi-criteria collaborative filtering recommender system based on optimization algorithms. The proposed method consists of an adaptable predictive model called multi-criteria inheritance-based prediction (MC-INH-BP). MC-INH-BP allows the customizing of the predictive model to suit the user context. Also, we propose a user profiling method called dynamic user interest print (D-UIP). The D-UIP stores the dynamic preferences of the users. The use of D-UIP reduces the impact of four challenges, critical users, tolerant users, dynamic opinion of the recurring users, and dynamic quality of the item. A set of experiments are conducted to compare MC-INH-BP with other single-criterion and multi-criteria collaborative filtering methods. The benchmark dataset, HotelExpedia, is used. The results prove the capability of MC-INH-BP to achieve better prediction accuracy regardless of the current context of the user. Besides, the results reveal that MC-INH-BP mitigates the cold start and sparsity issues. [ABSTRACT FROM AUTHOR]

The Online Roadshow, a new type of web application, is a digital marketing approach that aims to maximize contactless business engagement. It leverages web computing to conduct interactive game sessions via the internet. As a result, massive amounts of personal data are generated during the engagement process between the audience and the Online Roadshow (e.g., gameplay data and clickstream information). The high volume of data collected is valuable for more effective market segmentation in strategic business planning through data-driven processes such as web personalization and trend evaluation. However, the data storage and processing techniques used in conventional data analytic approaches are typically overloaded in such a computing environment. Hence, this paper proposed a new big data processing framework to improve the processing, handling, and storing of these large amounts of data. The proposed framework aims to provide a better dual-mode solution for processing the generated data for the Online Roadshow engagement process in both historical and real-time scenarios. Multiple functional modules, such as the Application Controller, the Message Broker, the Data Processing Module, and the Data Storage Module, were reformulated to provide a more efficient solution that matches the new needs of the Online Roadshow data analytics procedures. Some tests were conducted to compare the performance of the proposed frameworks against existing similar frameworks and verify the performance of the proposed framework in fulfilling the data processing requirements of the Online Roadshow. The experimental results evidenced multiple advantages of the proposed framework for Online Roadshow compared to similar existing big data processing frameworks. [ABSTRACT FROM AUTHOR]

With the increasing implementation of algorithms across various news platforms, understanding news consumers' subjective perceptions of algorithmic-based news recommender systems has become critical. A between-subjects experiment (News Recommender System type: content-based filtering vs. collaborative filtering vs. human editorial choice-based recommender system) with 161 participants revealed that participants tended to trust the collaborative filtering system and perceive news recommended by the system to be more credible and less biased compared to editorial choices-based or content-based recommender systems – due to the triggering of the homophily heuristic – even though the three systems recommended the same set of news. Implications were discussed. [ABSTRACT FROM AUTHOR]

The personalization feature has been implemented in various ways in educational games but the effectiveness of personalization feature on students' engagement was mixed in literature. Culture might be one possible reason but has been seldom explored in previous studies. This study filled in this gap by investigating the impact of the personalization feature on students' engagement patterns through the lens of culture. Results showed that the personalization game feature could engage students by capturing and maintaining students' attention and interest. Gender affected students' engagement patterns via cultural differences in attitudes toward time. Additionally, when the game was personalized, students' perceptions of student-teacher relation and group power would affect engagement patterns during gameplay and re-engagement in the future. Findings of this study demonstrated that the personalization game feature delivered via computers could be used to initiate and maintain students' engagement. Gender needs to be considered when utilizing games to engage students. Motivational design is needed to engage less active students in the personalized gaming environment. Students' cultural differences, such as perceptions of power distribution between students and instructors and power of group, need to be considered when designing personalized games. This study contributes to the field by explaining how gender influenced students' engagement patterns and why nonengagement was observed in some previous studies. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to determine how to influence the customer journey of mobile food ordering applications (MFOAs) users. It researches how available information could influence customers' intention to use MFOAs platforms in the prepurchase stage and explores the potential of personalized information to improve customer satisfaction with these services in the postpurchase stage. Design/methodology/approach: This research followed a mixed design, combining qualitative (focus groups) and quantitative (online survey) research and using both content analysis and partial least squares structural equation modeling. Findings: Two types of available information (firm-generated information and online customer reviews) had a positive influence on the behavioral intention to use MFOAs. Additionally, findings showed that different web personalization strategies, namely, content personalization, functional personalization and system-driven personalization, were useful tools to create customer satisfaction with this type of platform. Research limitations/implications: The study discusses limitations regarding the sample and sampling process, indicator variables and measures. Practical implications: The present research provides actionable insights for online food delivery providers. Originality/value: This study addresses a research gap in the literature and provides a novel and richer understanding of customer behavior toward mobile food delivery platforms. Also, it adds to the personalization research by identifying and testing a range of web personalization strategies. [ABSTRACT FROM AUTHOR]

Mass customization interfaces typically guide consumers through the configuration process in a sequential manner, focusing on one product attribute after the other. What if this standardized customization experience were personalized for consumers on the basis of how they process information? A series of large-scale field and experimental studies, conducted with Western and Eastern consumers, shows that matching the interface to consumers' culture-specific processing style enhances the effectiveness of mass customization. Specifically, presenting the same information isolated (by attribute) to Western consumers but contextualized (by alternative) to Eastern consumers increases satisfaction with and likelihood of purchasing the configured product, along with the amount of money spent on the product. These positive consumer responses emerge because of an increase in "interface fluency"—consumers' subjective experience of ease when using the interface. The authors advise firms to personalize the customization experience by employing processing-congruent interfaces across consumer markets. [ABSTRACT FROM AUTHOR]

Today, e-commerce has become a competitive space for online retailers. Therefore, personalization has become a vital part of e-commerce websites’ success that is a challenge for marketers and researchers. Therefore, this study aims to provide a model for web personalization and mining user interests using a hybrid approach of web-usage and web-content mining. So, the navigational patterns of web users and the interests of each user on web pages of a Persian website were extracted through web-usage mining and topic modeling, respectively. Users were then clustered using the dependency distribution algorithm and 25 categories were extracted. To better understand the behavioral patterns of web users, they were categorized using the Support Vector Machine algorithm based on the users’ interests and navigational behaviors. The most important result of the proposed system is that the patterns of users’ navigation are understandable and the subsequent analyzes will be much simpler.https://dorl.net/dor/20.1001.1.20088302.2022.20.3.9.6

Obtaining accurate information from social media during a crisis can be difficult, but should all information really be disseminated? Social media platforms actively filter out terrorist and violent extremist content (TVEC), but how are users themselves counteracting its spread? This paper aims to connect the research on media events with studies currently being conducted in information science and digital media research through a case study of tweets during the Vienna terror attack in late 2020. These tweets were manually coded in accordance with Braun and Clarke's reflexive thematic analysis. This study shows that during the 2020 Vienna attack, GIFs shared on Twitter served three functions: amplification, personalisation and ethical practice. The paper ends with a discussion on the ways cats may function as a countermeasure against the prevalence of TVEC on social media during terrorist attacks and the implications of such countermeasures. [ABSTRACT FROM AUTHOR]

By shining a light on the previously neglected combination of public service media (PSM) and the audience perspective, this paper adds to the debate on (algorithmic) news personalization. While news personalization may offer new opportunities, it can clearly also conflict with the PSM mission of universality of access, reach, and content. This empirical study compares the audience perspective on the news personalization of users and non-users of public service news in Germany, France, and the UK. Overall, the findings of the online survey show that users of public service news in Germany and the UK--in comparison to non-users of these services--perceive more risks such as missing out on certain topics or viewpoints, place greater value on a shared public sphere, and more strongly prefer a news selection primarily made by professional news editors. In France, however, the differences between users and non-users of public service news are rarely significant, which is interpreted against the background of the different media systems and the role of PSM. The findings add to the understanding of what public service news audiences expect PSM organizations to provide while keeping the difficult balance between personalization and universality. [ABSTRACT FROM AUTHOR]

Background: The use of serious games in health care is on the rise, as these games motivate treatment adherence, reduce treatment costs, and educate patients and families. However, current serious games fail to offer personalized interventions, ignoring the need to abandon the one-size-fits-all approach. Moreover, these games, with a primary objective other than pure entertainment, are costly and complex to develop and require the constant involvement of a multidisciplinary team. No standardized approach exists on how serious games can be personalized, as existing literature focuses on specific use cases and scenarios. The serious game development domain fails to consider any transfer of domain knowledge, which means this labor-intensive process must be repeated for each serious game. Objective: We proposed a software engineering framework that aims to streamline the multidisciplinary design process of personalized serious games in health care and facilitates the reuse of domain knowledge and personalization algorithms. By focusing on the transfer of knowledge to new serious games by reusing components and personalization algorithms, the comparison and evaluation of different personalization strategies can be simplified and expedited. In doing so, the first steps are taken in advancing the state of the art of knowledge regarding personalized serious games in health care. Methods: The proposed framework aimed to answer 3 questions that need to be asked when designing personalized serious games: Why is the game personalized? What parameters can be used for personalization? and How is the personalization achieved? The 3 involved stakeholders, namely, the domain expert, the (game) developer, and the software engineer, were each assigned a question and then assigned responsibilities regarding the design of the personalized serious game. The (game) developer was responsible for all the game-related components; the domain expert was in charge of the modeling of the domain knowledge using simple or complex concepts (eg, ontologies); and the software engineer managed the personalization algorithms or models integrated into the system. The framework acted as an intermediate step between game conceptualization and implementation; it was illustrated by developing and evaluating a proof of concept. Results: The proof of concept, a serious game for shoulder rehabilitation, was evaluated using simulations of heart rate and game scores to assess how personalization was achieved and whether the framework responded as expected. The simulations indicated the value of both real-time and offline personalization. The proof of concept illustrated how the interaction between different components worked and how the framework was used to simplify the design process. Conclusions: The proposed framework for personalized serious games in health care identifies the responsibilities of the involved stakeholders in the design process, using 3 key questions for personalization. The framework focuses on the transferability of knowledge and reusability of personalization algorithms to simplify the design process of personalized serious games. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology in various fields, the traditional accounting method is no longer applicable to the personalized development of e-commerce industry; Therefore, it is essential to improve the accounting method and construct a personalized recommendation model for e-commerce. Based on this background, this study firstly reconstructs the steps of accounting element recognition in the traditional accounting system and constructs an automated accounting recognition mechanism using BP neural network algorithm, aiming to improve the accuracy and efficiency of accounting element recognition; Secondly, a personalized e-commerce recommendation model based on multiple intelligence is built, which uses intelligent Q-learning algorithm to optimize the recommendation module, aiming to improve the accuracy of personalized recommendation. By comparing the performance of different accounting models under different personalized ecommerce systems, the accounting model proposed in this paper can predict the accounting entries well under the three-layer BP neural network, and the error between the maximum predicted value and the actual value is 0.23%. The recommendation model proposed in the study outperforms the traditional recommendation model and the recommendation model under collaborative filtering algorithm in predicting customers' personal preferences, whose predicted value is closer to the real situation. In summary, both the accounting method and the personalized recommendation model for e-commerce proposed in this study can achieve better application results, thus providing a new idea for the development of the e-commerce industry. [ABSTRACT FROM AUTHOR]

This article analyzes strategies and tactics used in online political communication. It examines political actors' communication traits and how they affect Facebook users' engagement. The focus of the article lies on different dimensions of personalization and how they are used as tactics. To answer these questions, I conducted a manual content analysis of posts from Barack Obama's and David Cameron's Facebook pages published from 2008 to 2016. Negative binomial regressions were used to estimate the effects of different predictors of users' engagement. The findings suggest that both leaders used Facebook primarily as a tool to communicate about political topics, mobilize supporters, and display their political and private virtues. In the context of personalization of communication, the study identifies three main levels of privatization--low, medium, and high. Furthermore, the analysis of users' interactions on Facebook pages (likes, comments, and shares) revealed that citizens preferred privatized and emotionalized posts. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is empowering personalized online news delivery to accommodate people's information needs and combat information overload. However, AI models learned from user data are inheriting and amplifying some underlying human prejudice such as the sentiment bias of news reading, which may lead to potential negative societal effects and ethical concerns. Here, substantial evidence shows that AI is manipulating the sentiment orientation of news displayed to users by promoting the presence chance of negative news, even if there is no human interference. To mitigate this manipulation, a sentiment-debiasing method based on a decomposed adversarial learning framework is proposed, which can reduce 97.3% of sentiment bias with only 2.9% accuracy sacrifice. Our work provides the potential in improving AI's responsibility in many human-centered applications such as online journalism and information spread. [ABSTRACT FROM AUTHOR]

When serious official political statements are not enough to get people's votes, politicians often turn to attention-grabbing and emotion-triggering self-presentation. They give the public access to their "normal, everyday" lives through personalisation and use other tools of "new" politics to create a favourable image of themselves. They can also show the unexpected, backstage aspects of their lives, such as their interactions with their pets. The paper analyses four case studies from different countries (Belarus, Estonia, Slovakia and Spain) in which politicians' references to their pets became a prominent topic in internet communication and provoked numerous humorous reactions in the form of memes. By looking at various degrees of politicians' personalisation strategies, we show that "new" and "old" politics should be regarded as the poles of a continuum rather than a binary opposition. We also discuss the content, form and stance of the humorous internet memes posted in reaction to the appearance of politicians' pets in the news. Our research indicates that such memes function to provoke a discussion and, as a result, form a polyvocal commentary on events; the politicians, however, must take risks accompanying unconventional, revealing political communication and hope that "there's no such thing as bad publicity". [ABSTRACT FROM AUTHOR]

The Enlightenment is an intellectual movement that emerged as a result of several centuries of processes in Western Europe. The main purpose of this movement is to save people from the tutelage and immaturity. In fact, where the process in which people use their own minds begins, the history of humanity underwent radical changes with the influence of other movements. In the environment where human societies and the systems that serve the society, underwent great changes, the education sector also went through the same processes. Post-enlightenment education, as a requirement of the main argument of enlightenment, was personalized and focused on the development of the individual. Educational models after this process have been in the understanding of educating people who are democratic, secular and fulfill their responsibilities towards their society and state. In addition, different philosophies and approaches in education emerged during this period. Contrary to previous periods, education has spread to the whole society and has been determined as a right that everyone will benefit equally. [ABSTRACT FROM AUTHOR]

Current definitions of friction in user experience generally advocate for reducing or eliminating friction in the pursuit of efficiency and ease of use. As the field of user experience design increasingly aims to develop highly personalized experiences for unique individuals, this narrow view of friction risks the vital role that friction plays in human life. This paper compares definitions of friction found in other fields and examines ways in which friction shows up in our day-to-day lives to suggest a working definition of friction that clarifies how the concept can be applied to the experience design challenges of tomorrow. To meet these challenges, researchers and designers need to shift from reflexively eliminating friction to intentionally designing friction, which requires asking deeper questions about human needs and goals in addition to examining how we might help people accomplish tasks quickly and easily. [ABSTRACT FROM AUTHOR]

In marketing, personalization is the action of designing and producing in ways that resonate with customer preferences. Content and products that are personalized according to customer preferences can reduce customer fatigue and time in making choices, thereby decreasing their cognitive load. Despite its importance, the literature on personalized marketing remains fragmented due to the absence of a comprehensive review that consolidates the intellectual structure of the field. This study bridges this knowledge gap through a bibliometric review using performance analysis and science mapping. Through a comprehensive review of 383 publications, this study reveals the publication and citation trends, the most prolific authors, journals, and publications, and six major themes (i.e., personalized recommendation, personalized relationship, personalization–privacy paradox, personalized advertising, personalization concept and discourse in marketing, and customer insights in personalized marketing) that characterize the body of knowledge of personalized marketing. The study concludes with future research directions as ways forward for personalized marketing, wherein a focus on new‐age technologies involving artificial intelligence, big data, blockchain, internet of things, and wearables is encouraged to explore new ways to curate personalized experiences across online and offline channels. [ABSTRACT FROM AUTHOR]

In this article, we propose a Latent Dirichlet Allocation-(LDA) based topic-graph probabilistic personalization model for Web search. This model represents a user graph in a latent topic graph and simultaneously estimates the probabilities that the user is interested in the topics, as well as the probabilities that the user is not interested in the topics. For a given query issued by the user, the webpages that have higher relevancy to the interested topics are promoted, and the webpages more relevant to the non-interesting topics are penalized. In particular, we simulate a user's search intent by building two profiles: A positive user profile for the probabilities of the user is interested in the topics and a corresponding negative user profile for the probabilities of being not interested in the the topics. The profiles are estimated based on the user's search logs. A clicked webpage is assumed to include interesting topics. A skipped (viewed but not clicked) webpage is assumed to cover some non-interesting topics to the user. Such estimations are performed in the latent topic space generated by LDA. Moreover, a new approach is proposed to estimate the correlation between a given query and the user's search history so as to determine how much personalization should be considered for the query. We compare our proposed models with several strong baselines including state-of-the-art personalization approaches. Experiments conducted on a large-scale real user search log collection illustrate the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

Today, e-commerce has become a competitive space for online retailers. Therefore, personalization has become a vital part of e-commerce websites' success, challenging marketers and researchers. This study aims to provide a model for web personalization and mining user interests using a hybrid web usage and web content mining approach. The navigational patterns of web users and the interests of each user on web pages of a Persian website were extracted through web usage mining and topic modeling. Users were then clustered using the dependency distribution algorithm, and 25 categories were extracted. To better understand the behavioral patterns of web users they were categorized using the Support Vector Machine algorithm based on the users' interests and navigational behaviors. The most important result of the proposed system is that the patterns of users' navigation are understandable, and the subsequent analyses will be much simpler. [ABSTRACT FROM AUTHOR]

Organizations analyze customers' personal data to understand and model their behavior. Identifying customers' gender is a significant factor in analyzing markets that help plan the promotional campaigns, determine target customers and provide relevant offers. Several techniques were developed to analyze different types of data, including text, image, speech, and biometrics, to identify the gender of the user. The method of synthesis of the profile name differs from one customer to another. Using numerical substitutions of specific letters, known as Leet language, impedes the gender identification task. Moreover, using acronyms, misspellings, and adjacent names impose additional challenges. Towards this goal, this work uses the customers' profile names associated with submitted reviews to recognize the customers' gender. First, we create datasets of profile names extracted from the customers' reviews. Secondly, we introduce a dynamic pruned n-gram model for identifying the gender of the user. It starts with data segmentation to handle adjacent parts, followed by data conversion and cleaning to fix the use of Leet language. Feature selection through a dynamic pruned n-gram model is the next step with the recurrent misspelling correction using fuzzy matching. We evaluate the proposed approach on the real data collected from active web resources. The obtained results demonstrate its validity and reliability. [ABSTRACT FROM AUTHOR]

Copyright of Actas de Diseño is the property of Facultad de Diseno y Comunicacion, Fundacion Universidad de Palermo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Conversational preference elicitation varies in system guidance and input restriction. • Recommendation satisfaction of CRSs positively influences user experience. • Recommendation performance of CRSs differs across preference elicitation methods. • User experience of chatbots should combine subjective and objective measures. The prevalence of conversational interfaces is rapidly rising, since improved algorithms allow for remarkable proficiency in understanding and generating natural language. This also holds for Conversational Recommender Systems (CRS), that benefit from information being provided by the user in the course of the dialogue to offer personalized recommendations. However, the challenge remains eliciting the user's characteristics and preferences in a way that leads to the most optimal user experience. Hence, the current research was aimed at investigating the effect of different Preference Elicitation (PE) methods on the user experience of a CRS. We introduce two axes across which PE methods can be classified, namely the degree of system prompt guidance and the level of user input restriction. We built three versions of a CRS to conduct a between-subjects experiment which compared three conditions: high guidance-high restriction, high guidance-low restriction and low guidance-low restriction. We tested their effect on ten constructs of user experience measures on 66 European participants, all working in agriculture or forestry. The study did not find any significant effects of the three preference elicitation methods on all user experience constructs collected through questionnaires. However, we did find significant differences in terms of the objective measures chat duration (Speed), response time (Cognitive Demand) and recommendation performance (Accuracy of Recommended Items). Regarding the recommendation performance, it was found that the preference elicitation methods with high guidance led to a higher match score than the condition with low guidance. The certainty score was highest in the condition with high guidance and high input restriction. Finally, we found through a question at the end of the conversation that users who were satisfied with the recommendation responded more positively to six out of ten user experience constructs. This suggests that satisfaction with the recommendation performance is a crucial factor in the user experience of CRSs. [ABSTRACT FROM AUTHOR]

Diversity is a crucial precondition for a democratic public discourse. In today's high-choice media environments, exposure to diverse news is largely determined by individuals' personal selection. Yet these decisions are increasingly shaped by online platforms, whose curation mechanisms may serve to expand or contract the diversity of encountered content. In a major extension of existing research, we show that positive short-term effects of platforms mask detrimental long-term effects. Drawing on a four-month tracking dataset and a comprehensive content analysis covering the online news consumption of over 10,000 German citizens, we demonstrate that even though short-term usage of platforms uniformly increases exposure diversity, long-term reliance can lead to decreases. In addition, platforms vary in their influences: News aggregators are beneficial to exposure diversity, while Twitter and search engines have a limiting effect; Facebook offers no significant influence. [ABSTRACT FROM AUTHOR]

New technologies are giving very interesting potential to the personalization of products and services, in particular of mobile services. The study of the literature shows that technology-based personalization is an important factor in the adoption of mobile services. However, previous researchers are not unanimous as to the nature of this role: some studies confirm the moderating or mediating effect of personalization on the relationship between the adoption factors stated in the Unified Technology Acceptance and Utilization Theory (UTAUT) and the intention to adopt technology-based services. Other researchers consider that personalization rather exerts a causal relationship on the adoption of technology-based services. This paper undertakes a literature review with the aim of clarifying the impact of new technologies, such as artificial intelligence, Big Data, Internet of Things and Block Chain, on the personalization of mobile banking services. Moreover, this study presents a synthesis of previous research examining the role of personalization in relation to the factors affecting adoption and the intention to adopt mobile banking. Finally, this research proposes a conceptual model that can serve as a basis for future empirical research in the context of mobile services. Results and discussion could guide future empirical research in this area. [ABSTRACT FROM AUTHOR]

Information technology and e‐commerce enable websites to provide personalized services based on consumer information, thereby providing convenience to consumers. However, the collection and use of consumer information may trigger concerns regarding privacy invasion. Grounded in exchange theory, the current study constructs a framework that links web personalization to consumer decisions (i.e., website loyalty and purchase intention) through consumer trust and reactance. The study also uses reactance theory as the theoretical basis to examine the moderating role of privacy concerns. An online survey was used to assess the effect of web personalization on consumer loyalty. The results show that consumers' privacy concerns negatively moderate the relationship between web personalization and website loyalty, and the influencing mechanism differs according to the levels of privacy concerns. More specifically, when consumers are less concerned about privacy, web personalization strengthens consumer trust to enhance loyalty. When consumers are more concerned about privacy, web personalization affects their loyalty through psychological reactance. The study findings show that although personalized services provide convenience to consumers, websites should carefully consider consumers' privacy issues. The trade‐off between web personalization and privacy concerns is necessary for a website's success. [ABSTRACT FROM AUTHOR]

Objective: This study investigates the relationship between personalization and customer experience, with emotion and cognitive as the components through which the result repeats purchase intention. Design/Methods/Approach: This study uses purposeful sampling to enroll research participants based on the research objective. A total of 429 respondents met the criteria, and the hypotheses were tested using Smart PLS 3 software with Partial least squares (PLS) structural equation modeling. Findings: The results of this study confirm that customer experience is a significant attribute of consumer behavior in the omnichannel context. Specifically, the main findings of our research indicate that emotions and cognitive processes are influenced by personalization, a relationship that has been established in the online context. The findings also indicate that the emotional and cognitive components of the customer experience can influence purchase intention. Originality: Our framework is based on previous research and applies to a new construct with new phenomena. Practical/Policy implication: Our finding demonstrates that a better customer experience in an omnichannel context has a beneficial effect on repeat purchase intention, indicating that personalization might influence purchasing behavior. In essence, our finding enables companies to understand the linked nature of variables and consequently prioritize marketing initiatives in an omnichannel context. [ABSTRACT FROM AUTHOR]

This study focuses on two specific privacy policies, namely privacy assurance and personalization declaration. Specifically, we investigate how these distinct privacy policies affect customers' privacy concerns and subsequent purchase responses. We have developed a conceptual model that addresses the independent effects of privacy assurance and personalization declaration, as well as the mechanism (i.e., privacy concerns) of these effects. Our model is grounded in motivation theory and supported by a field experiment and a controlled experiment. Our study demonstrates that privacy assurance that claims security protection negatively affects customers' purchase probability and purchase amount. By contrast, personalization declaration that addresses personalized benefits positively affects customers' purchase probability and purchase amount. Privacy concerns significantly mediate the negative effects of privacy assurance on purchase responses and the positive effects of personalization declaration on purchase responses. Overall, our findings inform managers of how to deploy privacy policies to reduce customers' privacy concerns and boost purchase responses. [ABSTRACT FROM AUTHOR]

Web recommendation systems are ubiquitous in the world used to overcome the product overload on e-commerce websites. Among various filtering algorithms, Collaborative Filtering and Content Based Filtering are the best recommendation approaches. Being popular, these filtering approaches still suffer from various limitations such as Cold Start Problem, Sparsity and Scalability all of which lead to poor recommendations. In this paper, we propose a hybrid system-based book recommendation system that anticipates recommendations. The proposed system is a mixture of collaborative filtering and content based filtering which can be explained in three phases: In the first phase, it identifies the users who are analogous to the active user by matching users' profiles. In the second phase, it chooses the candidate's item for every similar user by obtaining vectors Vc and Vm corresponding to the user's profile and the item contents. After calculating the prediction value for each item using the Resnick prediction equation, items are suggested to the target user in the final phase. We compared our proposed system to current state-of-the-art recommendation models, such as collaborative filtering and content-based filtering. It is shown in the experimental section that the proposed hybrid filtering approach outperforms conventional collaborative filtering and content-based filtering. [ABSTRACT FROM AUTHOR]