Recent Developments in Recommender Systems: A Survey

In this technical survey, we comprehensively summarize the latest advancements in the field of recommender systems. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. The study starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems, and then delves into the category of knowledge-based recommender systems. In addition, the survey analyzes the robustness, data bias, and fairness issues in recommender systems, summarizing the evaluation metrics used to assess the performance of these systems. Finally, the study provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field

Specifying Single-user and Collaborative Profiles for Alerting Systems

The 21st century is the age of information overload. Often, humans are incapable of processing all of the information that surrounds them and determining its relevance. The impact of overlooking crucial information ranges from annoying to fatal. Alerting systems help users deal with this vast amount of information by employing a push-based rather than a pull-based approach to information delivery. In this way, users receive the information they require at the appropriate moment. Users specify their alerting needs in a profile that is subscribed to the alerting system. The alerting system is continuously fed with data, and filters this data against all subscribed profiles. Whenever incoming data matches a profile, the subscriber is alerted. Although alerting systems solve the problem of information overload, the potential of these systems has not been fully put into practice. Alerting systems are either realised as dedicated systems that, at best, offer a set of possible profiles to choose from or, at worst, offer a preset profile for one purpose only. Alternatively, they are application frameworks that offer no support for the average user; that is, the specification of profiles is realised using a programming interface. Collaboration between users when specifying profiles is not supported. This thesis verifies the described situation by considering the example application domain of health care. Within this context, a requirements analysis was undertaken involving a patient-based online survey and interviews with health care providers. This analysis revealed the utility of alerting systems but a need for support for profile specification by end-users. It also identified the need for such a system to support the collaborative nature of health care. The shortcomings of alerting systems identified for the health-care area also exist in other domains. Hence, a variety of application areas will benefit from providing universal solutions to eliminate these shortcomings. Based on these findings, this thesis proposes the graphical profile specification language GPDL and an interactive single-user software tool that supports its use (GPDL-UI). The thesis introduces a novel collaborative alerting model for Information Systems. A collaborative extension of GPDL is implemented in the software tool CoastEd, an editor for the graphical specification of collaborative profiles. The developed languages and software tools target average users who have no expertise in specifying profiles involving logics and temporal constraints. The efficacy of the proposed languages and software were evaluated through three user studies. The first study examined interpretation and specification with GPDL. Based on the results of this first study, the single-user system GPDL-UI was designed and implemented and then evaluated in a second study. In turn, the lessons learned from the implementation and user studies for the single-user system influenced the development of the collaborative approach CoastEd; this editor was evaluated in the third study. The studies have shown that GPDL and GPDL-UI are suitable means for average users to effectively specify profiles in single-user alerting systems. High levels of accuracy were reached for specification and interpretation in both studies. GPDL-UI turned out to be a usable and effective software tool. The collaborative approach and CoastEd succeed in conveying the idea of collaborative profile specification to average users. Most types of collaborative profiles were successfully specified by users. For the initiator of the collaborative profile specification process, two types of profiles call for further research. Overall, the approach, languages and software tools developed are shown to be effective and merit future research in that area

A Collaborative Software Infrastructure based on the High Level Architecture and XML

A study is made of using the High Level Architecture (HLA) as foundation for distributed applications in the domain of Computer-Supported Collaborative Work. A plug-in, peer-to-peer infrastructure for such applications is proposed, aimed at facilitating development and management of collaborative software. Users of the framework collaborate in groups and sessions, described by a replicated state XML information model. A prototype infrastructure is developed, along with three prototype collaborative applications. Results of performance testing show that a transport system built on HLA compares reasonably well with a socket-based transport system. On the whole, results demonstrate feasibility of the infrastructure and of the objective of extending the HLA to non-simulation applications. Future work to adapt full-scale applications to the collaborative infrastructure is invited

Cosine similarity-based algorithm for social networking recommendation

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities

Avoiding overload in multiuser online applications

One way to strengthen the bond between popular applications and their online user communities is to integrate the applications with their communities, so users are able to observe and communicate with other users. The result of this integration is a Multiuser Online Application (MOA). The problem studied in this thesis is that MOA users and systems will be overloaded with information generated by large communities and complex applications. The solution investigated was to filter the amount of information delivered to users while attempting to preserve the benefits of dwelling in a MOA environment. This strategy was evaluated according to the amount of information it was capable of reducing and the effects as seen by MOA users. It was found that filtering could be used to substantially reduce the information exchanged by users while still providing users with the benefits of integrating application and community

Recommender Systems Based on Deep Learning Techniques

Tese de mestrado em Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020O atual aumento do número de opções disponíveis aquando a tomada de uma decisão, faz com que vários indivíduos se sintam sobrecarregados, o que origina experiências de utilização frustrantes e demoradas. Sistemas de Recomendação são ferramentas fundamentais para a mitigação deste acontecimento, ao remover certas alternativas que provavelmente serão irrelevantes para cada indivíduo. Desenvolver estes sistemas apresenta vários desafios, tornando-se assim uma tarefa de difícil realização. Para tal, vários sistemas (frameworks) para facilitar estes desenvolvimentos foram propostos, ajudando assim a reduzir os custos de desenvolvimento, através da oferta de ferramentas reutilizáveis, tal como implementações de estratégias comuns e modelos populares. Contudo, ainda é difícil encontrar um sistema (framework) que também ofereça uma abstração completa na conversão de conjuntos de dados, suporte para abordagens baseadas em aprendizagem profunda, modelos extensíveis, e avaliações reproduzíveis. Este trabalho introduz o DRecPy, um novo sistema (framework) que oferece vários módulos para evitar trabalho de desenvolvimento repetitivo, mas também para auxiliar os praticantes nos desafios mencionados anteriormente. O DRecPy contém módulos para lidar com: tarefas de carregar e converter conjuntos de dados; divisão de conjuntos de dados para treino, validação e teste de modelos; amostragem de pontos de dados através de estratégias distintas; criação de sistemas de recomendação complexos e extensíveis, ao seguir uma estrutura de modelo definida mas flexível; juntamente com vários processos de avaliação que originam resultados determinísticos por padrão. Para avaliar este novo sistema (framework), a sua consistência é analisada através da comparação dos resultados produzidos, com os resultados publicados na literatura. Para mostrar que o DRecPy pode ser uma ferramenta valiosa para a comunidade de sistemas de recomendação, várias características são também avaliadas e comparadas com ferramentas existentes, tais como extensibilidade, reutilização e reprodutibilidade.The current increase in available options makes individuals feel overwhelmed whenever facing a decision, resulting in a frustrating and time-consuming user experience. Recommender systems are a fundamental tool to solve this issue, filtering out the options that are most likely to be irrelevant for each person. Developing these systems presents us with a vast number of challenges, making it a difficult task to accomplish. To this end, various frameworks to aid their development have been proposed, helping reducing development costs by offering reusable tools, as well as implementations of common strategies and popular models. However, it is still hard to find a framework that also provides full abstraction over data set conversion, support for deep learning-based approaches, extensible models, and reproducible evaluations. This work introduces DRecPy, a novel framework that not only provides several modules to avoid repetitive development work, but also to assist practitioners with the above challenges. DRecPy contains modules to deal with: data set import and conversion tasks; splitting data sets for model training, validation, and testing; sampling data points using distinct strategies; creating extensible and complex recommenders, by following a defined but flexible model structure; together with many evaluation procedures that provide deterministic results by default. To evaluate this new framework, its consistency is analyzed by comparing the results generated by DRecPy against the results published by others using the same algorithms. Also, to show that DRecPy can be a valuable tool for the recommender systems’ community, several framework characteristics are evaluated and compared against existing tools, such as extensibility, reusability, and reproducibility

Intelligent Recommendation System for Higher Education

Education domain is very vast and the data is increasing every day. Extracting information from this data requires various data mining techniques. Educational data mining combines various methods of data mining, machine learning and statistics; which are appropriate for the unique data that comes from educational sector. Most of the education recommendation systems available help students to choose particular stream for graduate education after successful schooling or to choose particular career options after graduation. Counseling students during their course of graduate education will help him to comprehend subjects in better ways that will results in enhancing his understanding about subjects. This is possible by knowing the ability of student in learning subjects in past semesters and also mining the similar learning patterns from the past databases. Most educational systems allow students to plan out their subjects (particularly electives) during the beginning of the semester or course. The student is not fully aware about what subjects are good for his career, in which field he is interested in, or how would he perform. Recommending students to choose electives by considering his learning ability, his area of interest, extra-curricular activities and his performance in prerequisites would facilitate students to give a better performance and avoid their risk of failure. This would allow student to specialize in his domain of interest. This early prediction benefits the students to take necessary steps in advance to avoid poor performance and to improve their academic scores. To develop this system, various algorithms and recommendation techniques have to be applied. This paper reviews various data mining and machine learning approaches which are used in educational field and how it can be implemented