Towards Time-Aware Semantic enriched Recommender Systems for movies

Abstract. With the World Wide Web moving from passive to active, the role of recommender systems as an aid to make decisions play a very prominent role. This enables its users to find new items of high personal interest, which they were previously unaware of. While traditional approaches have shown the generation of high quality recommendations, the additional use of background knowledge to describe the items and their preferences on a more granular level is still lacking. Furthermore, these approaches do not take into consideration the contextual information, wherein the dimension 'time' plays a significant role. In this paper, we propose a new approach for recommending movies, which semantically enriches the process of generating recommendations by using a taxonomy derived out of different data sources from the LOD-Cloud. Furthermore, the paper also addresses the interplay between the rating behavior of the users and the dimension 'time'

Content Recommendation Through Linked Data

Nowadays, people can easily obtain a huge amount of information from the Web, but often they have no criteria to discern it. This issue is known as information overload. Recommender systems are software tools to suggest interesting items to users and can help them to deal with a vast amount of information. Linked Data is a set of best practices to publish data on the Web, and it is the basis of the Web of Data, an interconnected global dataspace. This thesis discusses how to discover information useful for the user from the vast amount of structured data, and notably Linked Data available on the Web. The work addresses this issue by considering three research questions: how to exploit existing relationships between resources published on the Web to provide recommendations to users; how to represent the user and his context to generate better recommendations for the current situation; and how to effectively visualize the recommended resources and their relationships. To address the first question, the thesis proposes a new algorithm based on Linked Data which exploits existing relationships between resources to recommend related resources. The algorithm was integrated into a framework to deploy and evaluate Linked Data based recommendation algorithms. In fact, a related problem is how to compare them and how to evaluate their performance when applied to a given dataset. The user evaluation showed that our algorithm improves the rate of new recommendations, while maintaining a satisfying prediction accuracy. To represent the user and their context, this thesis presents the Recommender System Context ontology, which is exploited in a new context-aware approach that can be used with existing recommendation algorithms. The evaluation showed that this method can significantly improve the prediction accuracy. As regards the problem of effectively visualizing the recommended resources and their relationships, this thesis proposes a visualization framework for DBpedia (the Linked Data version of Wikipedia) and mobile devices, which is designed to be extended to other datasets. In summary, this thesis shows how it is possible to exploit structured data available on the Web to recommend useful resources to users. Linked Data were successfully exploited in recommender systems. Various proposed approaches were implemented and applied to use cases of Telecom Italia

Recommender system to support comprehensive exploration of large scale scientific datasets

Bases de dados de entidades científicas, como compostos químicos, doenças e objetos astronómicos, têm crescido em tamanho e complexidade, chegando a milhares de milhões de itens por base de dados. Os investigadores precisam de ferramentas novas e inovadoras para auxiliar na escolha desses itens. Este trabalho propõe o uso de Sistemas de Recomendação para auxiliar os investigadores a encontrar itens de interesse. Identificamos como um dos maiores desafios para a aplicação de sistemas de recomendação em áreas científicas a falta de conjuntos de dados padronizados e de acesso aberto com informações sobre as preferências dos utilizadores. Para superar esse desafio, desenvolvemos uma metodologia denominada LIBRETTI - Recomendação Baseada em Literatura de Itens Científicos, cujo objetivo é a criação de conjuntos de dados , relacionados com campos científicos. Estes conjuntos de dados são criados com base no principal recurso de conhecimento que a Ciência possui: a literatura científica. A metodologia LIBRETTI permitiu o desenvolvimento de novos algoritmos de recomendação específicos para vários campos científicos. Além do LIBRETTI, as principais contribuições desta tese são conjuntos de dados de recomendação padronizados nas áreas de Astronomia, Química e Saúde (relacionado com a doença COVID-19), um sistema de recomendação semântica híbrido para compostos químicos em conjuntos de dados de grande escala, uma abordagem híbrida baseada no enriquecimento sequencial (SeEn) para recomendações sequenciais, um pipeline baseado em semântica de vários campos para recomendar entidades biomédicas relacionadas com a doença COVID-19.Databases for scientific entities, such as chemical compounds, diseases and astronomical objects, are growing in size and complexity, reaching billions of items per database. Researchers need new and innovative tools for assisting the choice of these items. This work proposes the use of Recommender Systems approaches for helping researchers to find items of interest. We identified as one of the major challenges for applying RS in scientific fields the lack of standard and open-access datasets with information about the preferences of the users. To overcome this challenge, we developed a methodology called LIBRETTI - LIterature Based RecommEndaTion of scienTific Items, whose goal is to create datasets related to scientific fields. These datasets are created based on scientific literature, the major resource of knowledge that Science has. LIBRETTI methodology allowed the development and testing of new recommender algorithms specific for each field. Besides LIBRETTI, the main contributions of this thesis are standard and sequence-aware recommendation datasets in the fields of Astronomy, Chemistry, and Health (related to COVID-19 disease), a hybrid semantic recommender system for chemical compounds in large-scale datasets, a hybrid approach based on sequential enrichment (SeEn) for sequence-aware recommendations, a multi-field semantic-based pipeline for recommending biomedical entities related to COVID-19 disease

Recommendations on the Internet of Things: Requirements, Challenges, and Directions

© 1997-2012 IEEE. The Internet of Things (IoT) is accelerating the growth of data available on the Internet, which makes the traditional search paradigms incapable of digging the information that people need from massive and deep resources. Furthermore, given the dynamic nature of organizations, social structures, and devices involved in IoT environments, intelligent and automated approaches become critical to support decision makers with the knowledge derived from the vast amount of information available through IoT networks. Indeed, IoT is more desirable of an effective and efficient paradigm of proactive discovering rather than postactive searching. This paper discusses some of the important requirements and key challenges to enable effective and efficient thing-of-interest recommendation and provides an array of new perspectives on IoT recommendation

Design Implications Towards Human-Centric Semantic Recommenders for Sustainable Food Consumption

The significance of food is evident in the myriad challenges confronting contemporary society, including the increasing prevalence of diet-related diseases, food waste with its adverse economic, environmental, and social impacts, and the significant impact of food production on environmental issues, among others. As the negative health and environmental impacts of dietary patterns become more evident, there is a growing demand for personalized and sustainable food recommendations to promote healthier and planet-friendly choices. This study aims to enrich the theoretical underpinnings of food recommender systems with an emphasis on sustainable food consumption, by integrating insights from existing research, behavior change theories, and Industry 5.0 digitization concepts on humanity-centered technologies.</p

A Systematic Literature Review of Linked Data-based Recommender Systems

Recommender Systems (RS) are software tools that use analytic technologies to suggest different items of interest to an end user. Linked Data is a set of best practices for publishing and connecting structured data on the Web. This paper presents a systematic literature review to summarize the state of the art in recommender systems that use structured data published as Linked Data for providing recommendations of items from diverse domains. It considers the most relevant research problems addressed and classifies RS according to how Linked Data has been used to provide recommendations. Furthermore, it analyzes contributions, limitations, application domains, evaluation techniques, and directions proposed for future research. We found that there are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications. The main ones are personalization of recommendations; use of more datasets considering the heterogeneity introduced; creation of new hybrid RS for adding information; definition of more advanced similarity measures that take into account the large amount of data in Linked Data datasets; and implementation of testbeds to study evaluation techniques and to assess the accuracy scalability and computational complexity of RS