UniNet: Next Term Course Recommendation using Deep Learning

Course enrollment recommendation is a relevant task that helps university students decide what is the best combination of courses to enroll in the next term. In particular, recommender system techniques like matrix factorization and collaborative filtering have been developed to try to solve this problem. As these techniques fail to represent the time-dependent nature of academic performance datasets we propose a deep learning approach using recurrent neural networks that aims to better represent how chronological order of course grades affects the probability of success. We have shown that it is possible to obtain a performance of 81.10% on AUC metric using only grade information and that it is possible to develop a recommender system with academic student performance prediction. This is shown to be meaningful across different student GPA levels and course difficultie

Sequential recommendation with metric models based on frequent sequences

Modeling user preferences (long-term history) and user dynamics (short-term history) is of greatest importance to build efficient sequential recommender systems. The challenge lies in the successful combination of the whole user's history and his recent actions (sequential dynamics) to provide personalized recommendations. Existing methods capture the sequential dynamics of a user using fixed-order Markov chains (usually first order chains) regardless of the user, which limits both the impact of the past of the user on the recommendation and the ability to adapt its length to the user profile. In this article, we propose to use frequent sequences to identify the most relevant part of the user history for the recommendation. The most salient items are then used in a unified metric model that embeds items based on user preferences and sequential dynamics. Extensive experiments demonstrate that our method outperforms state-of-the-art, especially on sparse datasets. We show that considering sequences of varying lengths improves the recommendations and we also emphasize that these sequences provide explanations on the recommendation.Comment: 25 pages, 6 figures, submitted to DAMI (under review

Previsão em séries temporais aplicada ao e-commerce

Dissertação de mestrado integrado em Engenharia InformáticaHistoricamente, as pessoas têm recorrido a amigos próximos ou experts para ajudar na tomada de decisão ou em recomendações sobre assuntos das mais diversas áreas. O crescimento da era digital nas últimas duas décadas, principalmente na web criou um overload de informação. No entanto, a nossa capacidade de avaliar as especificações de cada produto e escolher entre as enumeras alternativas existentes no mercado online, é limitada. Neste sentido, a ciência e tecnologia tem reagido adequadamente através do desenvolvimento de ferramentas para aliviar essa limitação. Os sistemas de recomendação são exemplos dessas ferramentas, que surgiram em meados dos anos 90 e têm tido muito sucesso. Os sistemas de recomendação funcionam como um atendimento personalizado. Numa situação de atendimento presencial apenas é possível apurar a pretensão do cliente após este a ter descrito. Estes sistemas visam optimizar e indicar a opção mais ajustada de acordo com o perfil do próprio cliente. Uma boa recomendação poderá ir ao encontro das pretensões de um determinado utilizador quando confrontado com uma plataforma e-commerce, o que faz com que o rácio utilizador/compra aumente. Além disso, quantos mais utilizadores satisfeitos com o sistema, mais popular e melhor o sistema se tornará, além de que será criada uma relação de proximidade entre o utilizador e o website. Existem vários métodos para gerar recomendações personalizadas para utilizadores específicos, mas o estado da arte de sistemas de recomendação são baseados na filtragem colaborativa. Estes sistema são amplamente utilizados por empresas como a Amazon, Netflix entre outras e tem obtido resultados muito significativos sem a necessidade de extrapolar as características dos artigos. Métodos ligados ao Deep Learning levaram a um elevado progresso em vários campos da Inteligência Artificial e, nos anos mais recentes, a um substancial número de propostas para melhorar sistemas de recomendação com Redes Neuronais Artificiais. Nesta dissertação, realizada na empresa Kodly Consulting, propõem-se a criação de uma solução baseada em redes neuronais recorrentes para geração de recomendações em ambientes e-commerce. Para que o sistema proposto seja facilmente integrado em qualquer aplicação e-commerce, pretende-se criar uma API que disponibiliza um conjunto de serviços úteis e fáceis de integrar.Historically, people have been asking friends or experts for help with decision-making or in getting recommendations on issues from the most diverse areas. The digital growth on the last decades, created a big overload of information. However, our ability to evaluate the specifications of each product and choose from the several alternatives available on the online market is limited. At the same time, science and technology has responded adequately by developing tools to mitigate and to help people in this limitation. Recommendation systems are examples of these tools, which emerged in the mid-1990s and have been very successful. A recommendation system works like a personalized service. In a face-to-face service situation, it is only possible to determine the customer’s claim after she/he has described it. These systems aim to optimize and indicate the most adjusted option according to the client’s own profile. A good recommen dation can meet the wishes of a certain user when faced with an e-commerce platform, which makes the user/purchase ratio increase. Furthermore, the more users satisfied with the system, the more popular and better the system will become, and a close relationship will be created between the user and the website. There are several methods to generate personalized recommendations for specific users, but state of the art recommendation systems is based on collaborative filtering. These systems are widely used by companies such as Amazon, Netflix and others and have obtained very significant results without the need to extrapolate the characteristics of the articles. Methods like Deep Learning have led to great progress in various fields of Artificial Intelligence and, in recent years, to a substantial number of proposals to improve recommendation systems with Artificial Neural Networks. In this dissertation, carried out at Kodly Consulting, we propose the creation of a solution based on recurrent neural networks to generate recommendations. In order to the proposed system be easily integrated into any e-commerce application, it is intended to create an API providing a set of useful and easy-to-integrate services

Long and Short-Term Recommendations with Recurrent Neural Networks

info:eu-repo/semantics/publishe