Dynamic and Super-Personalized Media Ecosystem Driven by Generative AI: Unpredictable Plays Never Repeating The Same

This paper introduces a media service model that exploits artificial intelligence (AI) video generators at the receive end. This proposal deviates from the traditional multimedia ecosystem, completely relying on in-house production, by shifting part of the content creation onto the receiver. We bring a semantic process into the framework, allowing the distribution network to provide service elements that prompt the content generator, rather than distributing encoded data of fully finished programs. The service elements include fine-tailored text descriptions, lightweight image data of some objects, or application programming interfaces, comprehensively referred to as semantic sources, and the user terminal translates the received semantic data into video frames. Empowered by the random nature of generative AI, the users could then experience super-personalized services accordingly. The proposed idea incorporates the situations in which the user receives different service providers' element packages; a sequence of packages over time, or multiple packages at the same time. Given promised in-context coherence and content integrity, the combinatory dynamics will amplify the service diversity, allowing the users to always chance upon new experiences. This work particularly aims at short-form videos and advertisements, which the users would easily feel fatigued by seeing the same frame sequence every time. In those use cases, the content provider's role will be recast as scripting semantic sources, transformed from a thorough producer. Overall, this work explores a new form of media ecosystem facilitated by receiver-embedded generative models, featuring both random content dynamics and enhanced delivery efficiency simultaneously.Comment: 13 pages, 7 figure

Semantic Brokering of Multimedia Contents for Smart Delivery of Ubiquitous Services in Pervasive Environments

With the proliferation of modern mobile devices having the capability to interact each other and with the environment in a transparent manner, there is an increase in the development of those applications that are specifically designed for pervasive and ubiquitous environments. Those applications are able to provide a service of interest for the user that depends on context information, such as the user's position, his preferences, the capability of the device and its available resources. Services have to respond in a rational way in many different situations choosing the actions with the best expected result by the user, so making environment not only more connected and efficient, but smarter. Here we present a semantic framework that provides the technology for the development of intelligent, context aware services and their delivery in pervasive and ubiquitous environments

Data management in audiovisual business: Netflix as a case study

Big data has become an enormous asset for on-demand content distribution services, helping information supply and decision- making, regarding both the content of the database and suscribers to the database. In this article we describe and define big data and data management in a media company devoted to on-demand audiovisual content distribution: Netflix. This article suggests that big data is a prime strategy in media business and outlines the upcoming challenges that follow its global expansion

Multi-dimensional clustering in user profiling

User profiling has attracted an enormous number of technological methods and applications. With the increasing amount of products and services, user profiling has created opportunities to catch the attention of the user as well as achieving high user satisfaction. To provide the user what she/he wants, when and how, depends largely on understanding them. The user profile is the representation of the user and holds the information about the user. These profiles are the outcome of the user profiling. Personalization is the adaptation of the services to meet the user’s needs and expectations. Therefore, the knowledge about the user leads to a personalized user experience. In user profiling applications the major challenge is to build and handle user profiles. In the literature there are two main user profiling methods, collaborative and the content-based. Apart from these traditional profiling methods, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, the profiling, achieved through these works, is lacking in terms of accuracy. This is because, all information within the profile has the same influence during the profiling even though some are irrelevant user information. In this thesis, a primary aim is to provide an insight into the concept of user profiling. For this purpose a comprehensive background study of the literature was conducted and summarized in this thesis. Furthermore, existing user profiling methods as well as the classification and clustering algorithms were investigated. Being one of the objectives of this study, the use of these algorithms for user profiling was examined. A number of classification and clustering algorithms, such as Bayesian Networks (BN) and Decision Trees (DTs) have been simulated using user profiles and their classification accuracy performances were evaluated. Additionally, a novel clustering algorithm for the user profiling, namely Multi-Dimensional Clustering (MDC), has been proposed. The MDC is a modified version of the Instance Based Learner (IBL) algorithm. In IBL every feature has an equal effect on the classification regardless of their relevance. MDC differs from the IBL by assigning weights to feature values to distinguish the effect of the features on clustering. Existing feature weighing methods, for instance Cross Category Feature (CCF), has also been investigated. In this thesis, three feature value weighting methods have been proposed for the MDC. These methods are; MDC weight method by Cross Clustering (MDC-CC), MDC weight method by Balanced Clustering (MDC-BC) and MDC weight method by changing the Lower-limit to Zero (MDC-LZ). All of these weighted MDC algorithms have been tested and evaluated. Additional simulations were carried out with existing weighted and non-weighted IBL algorithms (i.e. K-Star and Locally Weighted Learning (LWL)) in order to demonstrate the performance of the proposed methods. Furthermore, a real life scenario is implemented to show how the MDC can be used for the user profiling to improve personalized service provisioning in mobile environments. The experiments presented in this thesis were conducted by using user profile datasets that reflect the user’s personal information, preferences and interests. The simulations with existing classification and clustering algorithms (e.g. Bayesian Networks (BN), Naïve Bayesian (NB), Lazy learning of Bayesian Rules (LBR), Iterative Dichotomister 3 (Id3)) were performed on the WEKA (version 3.5.7) machine learning platform. WEKA serves as a workbench to work with a collection of popular learning schemes implemented in JAVA. In addition, the MDC-CC, MDC-BC and MDC-LZ have been implemented on NetBeans IDE 6.1 Beta as a JAVA application and MATLAB. Finally, the real life scenario is implemented as a Java Mobile Application (Java ME) on NetBeans IDE 7.1. All simulation results were evaluated based on the error rate and accuracy

Is Personality Key? Persuasive Effects of Prior Attitudes and Personality in Political Microtargeting

Messages that are designed to match a recipient’s personality, as enabled by microtargeting, have been found to influence political reasoning and even voting intentions. We extended these findings by adding prior attitudes to a microtargeting setting. Specifically, we examined what role different microtargeting approaches play in political reasoning by conducting an online experiment with a 2 (extraverted vs. introverted communication) × 2 (attitude-congruent vs. attitude-incongruent statement) between-subject design (N = 368). In line with the assumptions of the theory of motivated reasoning, attitude position matching emerged as an effective microtargeting strategy, and attitude strength moderated the effect of attitude congruency on recipients’ evaluations of political ads. While extraverted messages had no direct effect, that was unrelated to attitude congruency, recipients’ level of extraversion moderated the effect of extraverted communication on their evaluation of an ad. Interestingly, the intention to vote was significantly higher when an attitude-incongruent statement was phrased in an introverted rather than an extraverted manner, suggesting that information that challenges prior attitudes might be more persuasive when it is delivered in a more temperate way. In sum, the study indicates that matching message with personality alone might not be the most effective microtargeting approach within democratic societies

Hierarchical categorisation of tags for delicious

In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. In this technical report, we approach one of the practical aspects when it comes to represent users' interests from their tagging activity, namely the categorization of tags into high-level categories of interest. The reason is that the representation of user profiles on the basis of the myriad of tags available on the Web is certainly unfeasible from various practical perspectives; mainly concerning the unavailability of data to reliably, accurately measure interests across such fine-grained categorisation, and, should the data be available, its overwhelming computational intractability. Motivated by this, our study presents the results of a categorization process whereby a collection of tags posted at Delicious #http://delicious.com# are classified into 200 subcategories of interest.Preprin

Ethi(cs)quette of (Re)searching with E-friends: Clicking Towards a Social Media-driven Research Agenda

Social media increasingly shapes our professional and personal lives, leveraging its size, the potential for ubiquity, and real-time communication. Ranked the most popular social media platform by the number of subscribers, Facebook is increasingly gaining momentum as a research tool, mostly used to conduct surveys, adverts, and observation-driven research. However, Facebook’s potential for supporting consented qualitative research remains largely unexplored and deemed sometimes ethically questionable in the midst of ongoing debates around data protection rules and the ambiguity surrounding e-friendship meaning. This paper is based on an interpretative phenomenological Ph.D. study, between 2017-2020, aiming to deepen our understanding of London-based Romanian migrant entrepreneurs' experiences of social inclusion through entrepreneurship. This paper contributes to the literature on research methodology reflective practice of enabling ethical research, by outlining ethical implications of sampling via Facebook and when researching with e-friends as Facebook friends. It offers context-bound insights as guidance to researchers incorporating social media in their qualitative research The significance of this ethical research practice is discussed in terms of privacy, confidentiality, and informed consent as a cross point between GDPR regulatory framework, as universal research ethical framework, Facebook data privacy settings and the researcher’s reflective approach to mitigate ethical challenges experienced when recruiting Facebook e-friends

STATE OF SHOPPING AND THE VALUE OF INFORMATION: INSIGHTS FROM THE CLICKSTREAM

A critical challenge for online retailers is to determine what types of product and price information are best suited to influence online conversion. While it has long been known that customers differ in their state of shopping, it is cumbersome to learn about such latent differences offline. The availability of clickstream data however helps us in identifying meaningful segments of sessions on the basis of customers’ online behaviors. We examine whether product and price information had different impacts on customers belonging to three states of shopping, and also assess the effect on outcomes within a session and across sessions. Our results question the practice of offering price promotions to all customers of a store, and highlight the value of product information in increasing loyalty for some customers. Depending on the retailer’s goal– short term conversion versus longer-term customer relationship–a different information provision strategy is likely to be optimal