Algoritmo Híbrido de Recomendação

Nesta era tecnológica em que nos encontramos há cada vez mais informação disponível na internet, mas grande parte dessa informação não é relevante. Isto leva à necessidade de criar maneiras de filtrar informação, de forma a reduzir o tempo de recolha de informação útil. Esta necessidade torna o uso de sistemas de recomendação muito apelativo, visto estes personalizarem as pesquisas de forma a ajudar os seus utilizadores a fazer escolhas mais informadas. Os sistemas de recomendação procuram recomendar os itens mais relevantes aos seus utilizadores, no entanto necessitam de informação sobre os utilizadores e os itens, de forma a melhor os poder organizar e categorizar. Há vários tipos de sistemas de recomendação, cada um com as suas forças e fraquezas. De modo a superar as limitações destes sistemas surgiram os sistemas de recomendação híbridos, que procuram combinar características dos diferentes tipos de sistemas de recomendação de modo a reduzir, ou eliminar, as suas fraquezas. Uma das limitações dos sistemas de recomendação acontece quando o próprio sistema não tem informação suficiente para fazer recomendações. Esta limitação tem o nome de Cold Start e pode focar-se numa de duas áreas: quando a falta de informação vem do utilizador, conhecida como User Cold Start; e quando a falta de informação vem de um item, conhecida como Item Cold Start. O foco desta dissertação é no User Cold Start, nomeadamente na criação de um sistema de recomendação híbrido capaz de lidar com esta situação. A abordagem apresentada nesta dissertação procura combinar a segmentação de clientes com regras de associação. O objetivo passa por descobrir os utilizadores mais similares aos utilizadores numa situação de Cold Start e, através dos itens avaliados pelos utilizadores mais similares, recomendar os itens considerados mais relevantes, obtidos através de regras de associação. O algoritmo híbrido apresentado nesta dissertação procura e classifica todos os tipos de utilizadores. Quando um utilizador numa situação de Cold Start está à procura de recomendações, o sistema encontra itens para recomendar através da aplicação de regras de associação a itens avaliados por utilizadores no mesmo grupo que o utilizador na situação de Cold Start, cruzando essas regras com os itens avaliados por este último e apresentando as recomendações com base no resultado.Recommender systems, or recommenders, are a way to filter the useful information from the data, in this age where there is a lot of available data. A recommender system’s purpose is to recommend relevant items to users, and to do that, it requires information on both, data from users and from items, to better organise and categorise both of them. There are several types of recommenders, each best suited for a specific purpose, and with specific weaknesses. Then there are hybrid recommenders, made by combining one or more types of recommenders in a way that each type supresses, or at least limits, the weaknesses of the other types. A very important weakness of recommender systems occurs when the system doesn’t have enough information about something and so, it cannot make a recommendation. This problem known as a Cold Start problem is addressed in this thesis. There are two types of Cold Start problems: those where the lack of information comes from a user (User Cold Start) and those where it comes from an item (Item Cold Start). This thesis’ main focus is on User Cold Start problems. A novel approach is introduced in this thesis which combines clients’ segmentation with association rules. The goal is first, finding the most similar users to cold start users and then, with the items rated by these similar users, recommend those that are most suitable, which are gotten through association rules. The hybrid algorithm presented in this thesis finds and classifies all users’ types. When a user in a Cold Start situation is looking for recommendations, the system finds the items to recommend to him by applying association rules to the items evaluated by users in the same user group as the Cold Start user, crossing them with the few items evaluated by the Cold Start user and finally making its recommendations based on that

Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks

Identifying the topic-specific influential users in Twitter

Social Influence can be described as the ability to have an effect on the thoughts or actions of others. Influential members in online communities are becoming the new media to market products and sway opinions. Also, their guidance and recommendations can save some people the search time and assist their selective decision making. The objective of this research is to detect the influential users in a specific topic on Twitter. In more detail, from a collection of tweets matching a specified query, we want to detect the influential users, in an online fashion. In order to address this objective, we first want to focus our search on the individuals who write in their personal accounts, so we investigate how we can differentiate between the personal and non-personal accounts. Secondly, we investigate which set of features can best lead us to the topic-specific influential users, and how these features can be expressed in a model to produce a ranked list of influential users. Finally, we look into the use of the language and if it can be used as a supporting feature for detecting the author\u27s influence. In order to decide on how to differentiate between the personal and non-personal accounts, we compared between the effectiveness of using SVM and using a manually assembled list of the non-personal accounts. In order to decide on the features that can best lead us to the influential users, we ran a few experiments on a set of features inspired from the literature. Two ranking methods were then developed, using feature combinations, to identify the candidate users for being influential. For evaluation we manually examined the users, looking at their tweets and profile page in order to decide on their influence. To address our final objective, we ran a few experiments to investigate if the SLM could be used to identify the influential users\u27 tweets. For user account classification into personal and non-personal accounts, the SVM was found to be domain independent, reliable and consistent with a precision of over 0.9. The results showed that over time the list performance deteriorates and when the domain of the test data was changed, the SVM performed better than the list with higher precision and specificity values. We extracted eight independent features from a set of 12, and ran experiments on these eight and found that the best features at identifying influential users to be the Followers count, the Average Retweets count, The Average Retweets Frequency and the Age_Activity combination. Two ranking methods were developed and tested on a set of tweets retrieved using a specific query. In the first method, these best four features were combined in different ways. The best combination was the one that took the average of the Followers count and the Average Retweets count, producing a precision at 10 value of 0.9. In the second method, the users were ranked according to the eight independent features and the top 50 users of each were included in separate lists. The users were then ranked according to their appearance frequency in these lists. The best result was obtained when we considered the users who appeared in six or more of the lists, which resulted in a precision of 1.0. Both ranking methods were then conducted on 20 different collections of retrieved tweets to verify their effectiveness in detecting influential users, and to compare their performance. The best result was obtained by the second method, for the set of users who appeared in six or more of the lists, with the highest precision mean of 0.692. Finally, for the SLM, we found a correlation between the users\u27 average Retweets counts and their tweets\u27 perplexity values, which consolidates the hypothesis that SLM can be trained to detect the highly retweeted tweets. However, the use of the perplexity for identifying influential users resulted in very low precision values. The contributions of this thesis can be summarized into the following. A method to classify the personal accounts was proposed. The features that help detecting influential users were identified to be the Followers count, the Average Retweets count, the Average Retweet Frequency and the Age_Activity combination. Two methods for identifying the influential users were proposed. Finally, the simplistic approach using SLM did not produce good results, and there is still a lot of work to be done for the SLM to be used for identifying influential users

An Evaluation of the Use of Diversity to Improve the Accuracy of Predicted Ratings in Recommender Systems

The diversity; versus accuracy trade off, has become an important area of research within recommender systems as online retailers attempt to better serve their customers and gain a competitive advantage through an improved customer experience. This dissertation attempted to evaluate the use of diversity measures in predictive models as a means of improving predicted ratings. Research literature outlines a number of influencing factors such as personality, taste, mood and social networks in addition to approaches to the diversity challenge post recommendation. A number of models were applied included DecisionStump, Linear Regression, J48 Decision Tree and Naive Bayes. Various evaluation metrics such as precision, recall, ROC area, mean squared error and correlation coefficient were used to evaluate the model types. The results were below a benchmark selected during the literature review. The experiment did not demonstrate that diversity measures as inputs improve the accuracy of predicted ratings. However, the evaluation results for the model without diversity measures were low also and comparable to those with diversity indicating that further research in this area may be worthwhile. While the experiment conducted did not clearly demonstrate that the inclusion of diversity measures as inputs improve the accuracy of predicted ratings, approaches to data extraction, pre-processing, and model selection could inform further research. Areas of further research identified within this paper may also add value for those interested in this topic

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

Liver Segmentation Using Convolutional Neural Network Method with U-Net Architecture

Abnormalities in the liver can be used to identify the occurrence of disorders of the liver, one of which is called liver cancer. To detect abnormalities in the liver, segmentation is needed to take part of the liver that is affected. Segmentation of the liver is usually done manually with x-rays. . This manual detection is quite time consuming to get the results of the analysis. Segmentation is a technique in the image processing process that allocates images into objects and backgrounds. Deep learning applications can be used to help segment medical images. One of the deep learning methods that is widely used for segmentation is U-Net CNN. U-Net CNN has two parts encoder and decoder which are used for image segmentation. This research applies U-Net CNN to segment the liver data image. The performance results of the application of U-Net CNN on the liver image are very goodAccuracy performance obtained is 99%, sensitivity is 99%. The specificity is 99%, the F1-Score is 98%, the Jacard coefficient is 96.46% and the DSC is 98%.  The performance achieved from the application of U-Net CNN on average is above 95%, it can be concluded that the application of U-Net CNN is very good and robust in segmenting abnormalities in the liver. This study only discusses the segmentation of the liver image. The results obtained have not been applied to the classification of types of disorders that exist in the liver yet. Further research can apply the segmentation results from the application of U-Net CNN in the problem of classifying types of liver disorders