3 research outputs found
Towards the Use of Clustering Algorithms in Recommender Systems
Recommender Systems have been intensively used in Information Systems in the last decades, facilitating the choice of items individually for each user based on your historical. Clustering techniques have been frequently used in commercial and scientific domains in data mining tasks and visualization tools. However, there is a lack of secondary studies in the literature that analyze the use of clustering algorithms in Recommender Systems and their behavior in different aspects. In this work, we present a Systematic Literature Review (SLR), which discusses the different types of information systems with the use of the clustering algorithm in Recommender Systems, which typically involves three main recommendation approaches found in literature: collaborative filtering, content-based filtering, and hybrid recommendation. In the end, we did a quantitative analysis using K-means clustering for finding patterns between clustering algorithms, recommendation approaches, and some datasets used in their publications
An Efficient Recommendation System in E-commerce using Passer learning optimization based on Bi-LSTM
Recommendation system services have become crucial for users to access
personalized goods or services as the global e-commerce market expands. They
can increase business sales growth and lower the cost of user information
exploration. Recent years have seen a signifi-cant increase in researchers
actively using user reviews to solve standard recommender system research
issues. Reviews may, however, contain information that does not help consumers
de-cide what to buy, such as advertising or fictitious or fake reviews. Using
such reviews to offer suggestion services may reduce the effectiveness of those
recommendations. In this research, the recommendation in e-commerce is
developed using passer learning optimization based on Bi-LSTM to solve that
issue (PL optimized Bi-LSTM). Data is first obtained from the product
recommendation dataset and pre-processed to remove any values that are missing
or incon-sistent. Then, feature extraction is performed using TF-IDF features
and features that support graph embedding. Before submitting numerous features
with the same dimensions to the Bi-LSTM classifier for analysis, they are
integrated using the feature concatenation approach. The Collaborative Bi-LSTM
method employs these features to determine if the model is a recommended
product. The PL optimization approach, which efficiently adjusts the
classifier's parameters and produces an extract output that measures the
f1-score, MSE, precision, and recall, is the basis of this research's
contributions. As compared to earlier methods, the pro-posed PL-optimized
Bi-LSTM achieved values of 88.58%, 1.24%, 92.69%, and 92.69% for dataset 1,
88.46%, 0.48%, 92.43%, and 93.47% for dataset 2, and 92.51%, 1.58%, 91.90%, and
90.76% for dataset 3
O impacto da inteligência artificial no negócio eletrónico
Pela importância que a Inteligência Artificial exibe na atualidade, revela-se de grande interesse verificar até que ponto ela está a transformar o Negócio Eletrónico. Para esse efeito, delineou-se uma revisão sistemática com o objetivo de avaliar os impactos da proliferação destes instrumentos.
A investigação empreendida pretendeu identificar artigos científicos que, através de pesquisas realizadas a Fontes de Dados Eletrónicas, pudessem responder às questões de investigação implementadas: a) que tipo de soluções, baseadas na Inteligência Artificial (IA), têm sido usadas para melhorar o Negócio Eletrónico (NE); b) em que domínios do NE a IA foi aplicada; c) qual a taxa de sucesso ou fracasso do projeto. Simultaneamente, tiveram de respeitar critérios de seleção, nomeadamente, estar escritos em inglês, encontrarem-se no intervalo temporal 2015/2021 e tratar-se de estudos empíricos, suportados em dados reais.
Após uma avaliação de qualidade final, procedeu-se à extração dos dados pertinentes para a investigação, para formulários criados em MS Excel. Estes dados estiveram na base da análise quantitativa e qualitativa que evidenciaram as descobertas feitas e sobre os quais se procedeu, posteriormente, à sua discussão. A dissertação termina com as conclusão e discussão de trabalhos futuros.Due to the importance that Artificial Intelligence exhibits today, it is of great interest to see to what extent it is transforming the Electronic Business. To this end, a systematic review was designed to evaluate the impacts of the proliferation of these instruments.
The research aimed to identify scientific articles that, through research carried out on Electronic Data Sources, could answer the research questions implemented: a) what kind of solutions, based on Artificial Intelligence, have been used to improve the Electronic Business; b) in which areas of the Electronic Business Artificial Intelligence has been applied; c) what the success rate or failure of the project is. At the same time, they must comply with selection criteria, to be written in English, to be found in the 2015/2021-time interval and to be empirical studies supported by actual data.
After a final quality evaluation, the relevant data for the investigation were extracted for forms created in MS Excel. These data were the basis of the quantitative and qualitative analysis that evidenced the findings found and on which they were subsequently discussed. The dissertation ends with the conclusion and discussion of future works