From Music to Museum: Applications of Multi-Objective Ant Colony Systems to Real World Problems

International audienceRecommender systems are a flourishing domain in computer science for almost 30 years now. This rising popularity follows closely the number of data collected all around the world. Each and every internet user produces a huge amount of content during his lifetime. Recommender systems proactively help users to navigate these pieces of information by gathering, and selecting the items to users' needs. In this paper, we discuss the possibility and interest of applying our Multi-Objective Ant Colony System called AntRS to recommend items in different application domains. In particular, we show how our model performs better than the state-of-the-art models with music dataset, and describe our work-in-progress with the museum of fine arts in Nancy (France). The motivation behind this change of application domain is the recommendation of progressive sequences rather than unordered lists of items

Explicação de Recomendações com Diversificação: uma Revisão Bibliográfica

Esse artigo a apresenta o resultado de uma revisão bibliográfica  sobre explicação de recomendação com diversificação. Constatou-se com  base nela que nenhuma pesquisa propôs ainda estudar como gerar explicação de recomendação com diversificação. Foram encontrados apenas trabalhos que indicam a necessidade de haver explicação de recomendação com diversificação. A partir dessa constatação de necessidade de pesquisa propõe-se, como trabalho futuro, investigar e desenvolver uma abordagem de  explicação de recomendação com diversificação. Essa abordagem terá que  gerar explicações que sejam interpretáveis e persuasivas de modelos  complexos de recomendação baseados em algoritmos de aprendizagem de máquina. Para avaliação experimental da abordagem de explicação de recomendação há possibilidade, dentro do grupo de pesquisa de Sistemas de  Informação da UFRGS, de aplicar e avaliar essa abordagem de explicação no domínio de Cidades Inteligentes

Social media and e-commerce: A scientometrics analysis

he purpose of this research is to investigate the status and the evolution of the scientific studies on the effect of social networks on e-commerce. The study seeks to address the status of a set of scientific productions of researchers in the world indexed in Scopus based on scientometrics indicators. In total, 1926 articles were found and the collected data were analyzed using quantitative and qualitative indicators of scientometrics with bibliometrix R software package. The findings show that researches have grown exponentially since 2009 and the trend has continued at relatively stable rates. Thematic analysis shows that the subject had a significant but not well-developed research field. There is a high rate of cooperation with a rich research network among institutions in United States, European and Asian countries. Studies also show that research interest in this area is prevalent in developed countries. In addition, the lack of funds and complex analytical tools may be due to lack of studies in developing countries, especially in Africa. The study of the global trend of research through scientometrics helps managers and researchers in identifying countries and institutions with the greatest potential for scientific production, which allows them to develop their professions

Music Recommendations

International audienceToday's online music services like Spotify provide their listeners with different types of music recommendations, e.g., in the form of weekly recommendations or personalized radio stations. Such recommendations are often based, at least in parts, on collaborative filtering techniques. In this chapter, we first review the different types of music recommendations that can be found in practice and discuss the specific challenges of the domain. Next, we discuss technical approaches for the problems of music discovery and next-track recommendation in more depth, with a specific focus on their practical application at Spotify. Finally, we further elaborate on open challenges in the field and revisit the specific problems of evaluating music recommendation systems in academic environments

Intra-list similarity and human diversity perceptions of recommendations: the details matter

The diversity of the generated item suggestions can be an important quality factor of a recommender system. In offline experiments, diversity is commonly assessed with the help of the intra-list similarity (ILS) measure, which is defined as the average pairwise similarity of the items in a list. The similarity of each pair of items is often determined based on domain-specific meta-data, e.g., movie genres. While this approach is common in the liter- ature, it in most cases remains open if a particular implementation of the ILS measure is actually a valid proxy for the human diversity perception in a given application. With this work, we address this research gap and investi- gate the correlation of different ILS implementations with human perceptions in the domains of movie and recipe recommendation. We conducted several user studies involving over 500 participants. Our results indicate that the particularities of the ILS metric implementation matter. While we found that the ILS metric can be a good proxy for human perceptions, it turns out that it is important to individually validate the used ILS metric implementation for a given application. On a more general level, our work points to a certain level of oversimplification in recommender systems research when it comes to the design of computational proxies for human quality perceptions and thus calls for more research regarding the validation of the corresponding metrics

Aplica??o do NSGA-II em uma abordagem multiobjetivo na recomenda??o de objetos de aprendizagem em ambientes inteligentes para educa??o

Existem grandes reposit?rios de conte?dos na Internet que podem ser utilizados como fonte de recursos para promo??o do e-learning. Por?m, o volume de materiais dispon?veis ? muito grande. Para lidar com essa quantidade de itens, s?o utilizados os Sistemas de Recomenda??o, que selecionam os materiais mais adequados aos objetivos do estudante. Nesse contexto, o presente trabalho prop?e uma abordagem multiobjetivo do problema de recomenda??o de Objetos de Aprendizagem (OAs) para atender a uma demanda de ensino, desde uma pequena se??o de aprendizagem at? um curso completo. Na abordagem proposta, uma solu??o n?o necessariamente cobre todos os conceitos estabelecidos pelo design instrucional. Na verdade, buscam-se solu??es que tenham o menor custo e a maior quantidade de conceitos cobertos. O custo de cada OA ? diferente para cada aluno, determinado a partir do seu estilo de aprendizagem e de avalia??es feitas por outros estudantes com perfil semelhante daquele aluno. Todavia, existem depend?ncias entre alguns conceitos estabelecidas pelo design instrucional que cada solu??o deve respeitar. A partir do conjunto de solu??es geradas, o estudante escolhe aquela que melhor atende as suas expectativas. Para se obter solu??es foi utilizado o algoritmo NSGA II no framework MOEA, testado em uma inst?ncia de problema gerada artificialmente. Foram criados dois m?todos de inicializa??o da popula??o, que determina para cada indiv?duo valores aleat?rios de cobertura e seleciona Objetos de Aprendizagem tamb?m de forma rand?mica. Os resultados obtidos foram conjuntos de at? 19 solu??es para inst?ncias com 200 conceitos e 10 depend?ncias entre conceitos, bem como de at? 6 solu??es para inst?ncia com 20 conceitos e 2 depend?ncias. Para cursos com 20 depend?ncias, a quantidade de solu??es obtidas foi menor, no m?ximo 10. Mostra-se importante a avalia??o de outros algoritmos al?m do NSGA-II, bem como a necessidade de aprimoramento do algoritmo de gera??o da popula??o inicial para obter mais solu??es inicialmente vi?veis. Um poss?vel trabalho futuro ? aplicar o problema em reposit?rios reais, no qual o custo de cada OA ? obtido a partir de seus pr?prios atributos.Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2020.There are large repositories of content in the Internet that can be used as resources for e-learning. However, there are too much available materials. Recommender Systems are used to deal with these many items, which select the most suitable materials for the student?s goals. The present paper proposes a multi-objective approach to the problem of Learning Objects (LOs) recommendation to meet a teaching demand, from a small learning section to an entire course. In the proposed approach, a solution does not necessarily cover all course subjects, established by the instructional design. In fact, solutions for a course are sought that have the lowest cost and the largest number of subjects covered. The cost of each LO is different for each student, based on his/her learning style, as well as assessments made by other students with a similar profile to that student. However, the instructional design also establishes dependencies between some subjects that each solution must respect. From the set of generated solutions, the student chooses the one that best meets his/her preferences and expectations. An artificial instance of that problem was tested, using the NSGA II algorithm in the MOEA framework. Two methods of population initialization were created, which determine for each individual random values of coverage and select Learning Objects randomly as well. The results obtained were sets of up to 19 solutions for instances of courses with 200 subjects and 10 dependencies, as well as sets up to 6 solutions for instance of courses with 20 subjects and 2 dependencies. For courses with 20 dependencies, the amount of solution obtained was smaller, at most 10. It?s important to evaluate other algorithms in addition to the NSGA-II, as well as the need to improve the initial population generation algorithm to obtain more feasible solutions. A possible future work is to apply the problem in real repositories, in which the cost of each OA is estimated based on its attributes