AI models for recommendation

Ponencia presentada en EMAI2021, West Bengal, India, 4/4/2021[EN]Today, the industries of all European countries face common challenges: improving resource efficiency, becoming more environmentally friendly, mitigating climate change, improving the digitization in all segments of the value chain and improving transparency and safety, providing consumers with detailed information and ensuring the safety and quality of the final product. Growing concerns about environmental and social issues are pushing the demands of stakeholders (customers, workers, shareholders, consumers, etc.) and the public towards more sustainable processes and products. Sustainability is closely linked to climate change: the introduction of sustainable measures, both by consumers and producers, is inherently a measure against climate change

Recommendation AI models: case studies

Seminario presentado en EMAI2021, West Bengal, India, 4/4/2021[EN] The targeted consumers can be not only individuals sensitive to environmental and sustainable consumption issues, but also communities, small businesses (e.g., local coffee shop, school, sports club) that share the same concerns as their customers or are just trying to better address their needs. In addition, this tool is designed to assist decision-makers in companies (e.g., supply chain and purchasing managers) as well as policy makers in assessing the overall sustainability of products. Likewise, the tool can provide valuable information to manufacturers who, based on the "sustainable market momentum" gained, could innovate their products and their approach to improving sustainability, thus differentiating themselves from the competitio

Intelligent models for recommendation

Seminario presentado en EMAI2021, West Bengal, India, 4/4/2021[EN]Information tools are one of the types of tools available in an effort to change consumers' perceptions, motivations, knowledge and standards. Accordingly, it is increasingly important for consumers to be able to make informed choices about the products they buy, especially in terms of sustainability. Together with the commitment of businesses and organizations to more responsible and sustainable processes and production, the implementation of the European Green Deal and the Sustainable Development Goals is an urgent challenge to all actors in society to contribute to changing the way we meet our needs

ECLAP 2012 Conference on Information Technologies for Performing Arts, Media Access and Entertainment

It has been a long history of Information Technology innovations within the Cultural Heritage areas. The Performing arts has also been enforced with a number of new innovations which unveil a range of synergies and possibilities. Most of the technologies and innovations produced for digital libraries, media entertainment and education can be exploited in the field of performing arts, with adaptation and repurposing. Performing arts offer many interesting challenges and opportunities for research and innovations and exploitation of cutting edge research results from interdisciplinary areas. For these reasons, the ECLAP 2012 can be regarded as a continuation of past conferences such as AXMEDIS and WEDELMUSIC (both pressed by IEEE and FUP). ECLAP is an European Commission project to create a social network and media access service for performing arts institutions in Europe, to create the e-library of performing arts, exploiting innovative solutions coming from the ICT

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

Unmute This: Circulation, Sociality, and Sound in Viral Media

Cats at keyboards. Dancing hamsters. Giggling babies and dancing flashmobs. A bi-colored dress. Psy’s “Gangnam Style” music video. Over the final decade of the twentieth century and the first decades of the twenty-first, these and countless other examples of digital audiovisual phenomena have been collectively adjectivally described through a biological metaphor that suggests the speed and ubiquity of their circulation—“viral.” This circulation has been facilitated by the internet, and has often been understood as a product of the web’s celebrated capacities for democratic amateur creation, its facilitation of unmediated connection and sharing practices. In this dissertation, I suggest that participation in such phenomena—the production, watching, listening to, circulation, or “sharing” of such objects—has constituted a significant site of twenty-first-century musical practice. Borrowing and adapting Christopher Small’s influential 1998 coinage, I theorize these strands of practice as viral musicking. While scholarship on viral media has tended to center on visual parameters, rendering such phenomena silent, the term “viral musicking” seeks to draw media theory metaphors of voice and listening into dialogue with musicology, precisely at the intersection of audiovisual objects which are played, heard, listened to. The project’s methodology comprises a sonically attuned media archeology, grounded in close readings of internet artifacts and practices; this sonic attunement is afforded through musicological methods, including analyses of genre, aesthetics, and style, discourse analysis, and twenty-first-century reception (micro)histories across a dynamic media assemblage. By analyzing particular ecosystems of platforms, behavior, and devices across the first decades of the twenty-first century, I chart a trajectory in which unpredictable virtual landscapes were tamed into entrenched channels and pathways, enabling a capacious “virality” comprising disparate phenomena from simple looping animations to the surprise release of Beyoncé’s 2013 album. Alongside this narrative, I challenge utopian claims of Web 2.0’s digital democratization by explicating the iterative processes through which material, work, and labor were co-opted from amateur content creators and leveraged for the profit of established media and corporate entities. “Unmute This” articulates two main arguments. First, that virality reified as a concept and set of dynamic-but-predictable processes over the course of the first decades of the twenty-first century; this dissertation charts a cartography of chaos to control, a heterogeneous digital landscape funneled into predictable channels and pathways etched ever more firmly and deeply across the 2010s. Second, that analyzing the musicality of viral objects, attending to the musical and sonic parameters of virally-circulating phenomena, and thinking of viral participation as an extension of musical behavior provide a productive framework for understanding the affective, generic, and social aspects of twenty-first-century virality. The five chapters of the dissertation present analyses of a series of viral objects, arranged roughly chronologically from the turn of the twenty-first century to the middle of the 2010s. The first chapter examines the loops of animated phenomena from The Dancing Baby to Hampster Dance and the Badgers animation; the second moves from loops to musicalization, considering remixing approaches to the so-called “Bus Uncle” and “Bed Intruder” videos. The third chapter also deals with viral remixing, centering around Rebecca Black’s “Friday” video, while the fourth chapter analyzes “unmute this” video posts in the context of the mid-2010s social media platform assemblage. The final chapter presents the 2013 surprise release of Beyoncé’s self-titled visual album as an apotheosis to the viral narratives that precede it—a claim that is briefly interrogated in the dissertation’s epilogue