Recommender systems have gained a lot of popularity in recent times dueto their application in the wide range of fields. Recommender systems areintended to support users in finding the relevant items based on their interestsand preferences. Recommender algorithms proposed by researchersevolved over time from simple matching recommendations to machine learningalgorithms. One such class of algorithms with increasing focus is oncalled streaming recommender systems, these algorithms treat input data asa stream of events and make recommendations. To evaluate the algorithmsthat work with continuous data streams, stream-based evaluation techniquesare needed. So far, less interest is shown in the research so far on the evaluationof recommender systems in streaming environments.In this thesis, a simple evaluation framework named StreamER that evaluatesrecommender algorithms that work on streaming data is proposed.StreamER is intended for the rapid prototyping and evaluation of incrementalalgorithms. StreamER is designed and implemented using object-orientedarchitecture to make it more flexible and expandable. StreamER can beconfigured via a configuration file, which can configure algorithms, metricsand other properties individually. StreamER has inbuilt support for calculatingaccuracy metrics, namely click-through rate, precision, and recall.The popular-seller and random recommender are two algorithms supportedout of the box with StreamER. Evaluation of StreamER is performed via acombination of hypothesis and manual evaluation. Results have matched theproposed hypothesis, thereby successfully evaluating the proposed frameworkStreamER.

Vulnerable code may cause security breaches in software systems resulting in financial and reputation losses for the organizations in addition to loss of their customers’ confidential data. Delivering proper software security training to software developers is key to prevent such breaches. Conventional training methods do not take the code written by the developers over time into account, which makes these training sessions less effective. We propose a method for recommending computer–security training to help identify focused and narrow areas in which developers need training. The proposed method leverages the power of static analysis techniques, by using the flagged vulnerabilities in the source code as basis, to suggest the most appropriate training topics to different software developers. Moreover, it utilizes public vulnerability repositories as its knowledgebase to suggest community accepted solutions to different security problems. Such mitigation strategies are platform independent, giving further strength to the utility of the system. This research discussed the proposed architecture of the recommender system, case studies to validate the system architecture, tailored algorithms to improve the performance of the system, and human subject evaluation conducted to determine the usefulness of the system. Our evaluation suggests that the proposed system successfully retrieves relevant training articles from the public vulnerability repository. The human subjects found these articles to be suitable for training. The human subjects also found the proposed recommender system as effective as a commercial tool.

This study was conducted to investigate how well deep learning can be applied to the field of tag recommender systems. In the context of an image item, tag recommendations can be given based on tags already existing on the item, or on item content information. In the current literature, there are no works which jointly models the tags and the item content information using deep learning. Two tag recommender systems were developed. The first one was a highly optimized hybrid baseline model based on matrix factorization and Bayesian classification. The second one was based on deep learning. The two models were trained and evaluated on a dataset of user-tagged images and videos from Flickr. A percentage of the tags were withheld, and the evaluation consisted of predicting them. The deep learning model attained the same prediction recall as the baseline model in the main evaluation scenario, when half of the tags were withheld. However, the baseline model generalized better to the sparser scenarios, when a larger number of tags were withheld. Furthermore, the computations of the deep learning model were much more time-consuming than the computations of the baseline model. These results led to the conclusion that the baseline model was more practical, but that there is much potential in using deep learning for the purpose of tag recommendation. / Den här studien genomfördes i syfte att undersöka hur effektivt djupinlärning kan användas för att konstruera rekommendationssystem för taggar. När det gäller bildobjekt så kan taggar rekommenderas baserat på taggar som redan förekommer på objektet, samt på information om objektet. I dagens forskning finns det inte några publikationer som presenterar ett rekommendationssystem baserat på djupinlärning som bygger på att gemensamt använda taggarna och objektsinformationen. I studien har två rekommendationssystem utvecklats. Det första var en referensmodell, ett väloptimerat hybridsystem baserat på matrisfaktorisering och bayesiansk klassificering. Det andra systemet baserades på djupinlärning. De två modellerna tränades och utvärderades på en datamängd med bilder och videor taggade av användare från Flickr. En procentandel av taggarna var undanhållna, och utvärderingen gick ut på att förutsäga dem. Djupinlärningsmodellen gav förutsägelser av samma kvalitet som referensmodellen i det primära utvärderingsscenariot, där hälften av taggarna var undanhållna. Referensmodellen gav dock bättre resultat i de scenarion där alla eller nästan alla taggar var undanhållna. Dessutom så var beräkningarna mycket mer tidskrävande för djupinlärningsmodellen jämfört med referensmodellen. Dessa resultat ledde till slutsatsen att referensmodellen var mer praktisk, men att det finns mycket potential i att använda djupinlärningssystem för att rekommendera taggar.

With an overwhelming number of movies available globally, it can be a daunting task for users to find movies that cater to their individual preferences. The vast selection can often leave people feeling overwhelmed, making it challenging to pick a suitable movie. As a result, movie service providers need to offer a recommendation system that adds value to their customers. A movie recommendation system can help customers in this regard by providing a process that assists in finding movies that match their preferences. Previous studies on recommendation systems that use Machine Learning (ML) algorithms have demonstrated that these algorithms outperform some of the existing recommendation methods regarding recommendation strategy. However, there is still room for further improvement, especially when it comes to exploring scenarios where users need to spend a considerable amount of time finding movies related to their preferred genres. This prolonged search for the right movies can give rise to problems such as data sparsity and cold start. To address these issues, we propose a machine learning-based recommender system for movie genres using the K-nearest Neighbours (KNN) algorithm. Our final system utilizes a slider bar on a Streamlit web app, allowing users to select their preferred movies and see recommendations for similar movies. By incorporating user preferences, our system provides personalized recommendations that are more likely to meet the user's interests and preferences. To address our research question: “How and to what extent can a machine learning-based recommender system be developed focusing on movie genres where movie popularity can be predicted based on its content?” we propose three main research objectives. Firstly, we investigate the employment of a classification algorithm in recommending movies focusing on interest genres. Secondly, we evaluate the performance of our classification algorithm concerning movie viewers. Thirdly, we represent the popularity of movie genres based on the content and investigate how this representation can inform the movie recommendation algorithm. On the heels of an experimental strategy, we extract and pre-process a dataset of movies and their associated genre labels from Kaggle. The dataset consists of two files derived from The Movie Database (TMDB) 5000 Movie Dataset. We develop a machine learning-based recommender system based on the similarity of movie genres using the extracted and pre-processed dataset. We vary the KNN algorithm with a slider bar to recommend movies of varying similarity to the selected movie, ranging from similar to diverse in genre. This approach can suggest movies with different titles for users with diverse preferences. We evaluate the performance of the KNN classification algorithm using a user's interest genres, measuring its accuracy, precision, recall, and F1-score. The algorithm's accuracy ranges from low to moderate across different values of K, indicating its moderate effectiveness in predicting user preferences. The algorithm's precision ranges from moderate to high, implying that it provides accurate recommendations to the user. The recall score improves with increasing K and reaches its maximum at K=15, demonstrating its ability to retrieve relevant recommendations. The algorithm achieves a good balance between precision and recall, with an average F1-score of 0.60. This means that the algorithm can accurately identify relevant movies and recommend them to users with a high degree of accuracy. Furthermore, our result shows that the popularity visualization technique using KNN is a powerful tool for analysing and understanding the popularity of different movie genres, which can inform important decisions related to marketing, distribution, and production in the movie industry. In conclusion, our machine learning-based recommender system using KNN for movie genres is a game changer. It allows users to select their preferred movies and see recommendations for similar movies using a slider bar on a Streamlit web app. If confirmed by future research, the promising findings of this thesis can pave the way for developing and incorporating other classification algorithms and features for movie recommendation and evaluation. Furthermore, the adjustable slider bar ranges on the Streamlit web app allow users to customize their movie preferences and receive tailored recommendations.

Recommender Systems are information filtering systems that aim to predict a user’s preference for an item. A central challenge when building a Recommender System is the user cold-start, the integration of new users into the recommendation process. It can currently not be ultimately solved, but only mitigated based on additional information about the user. This work proposes to utilize technical usage data, telemetry data, for user preference modeling. In the industry use-case of an in-game item recommendation system for a mobile game, telemetric features have been engineered, to capture player’s behavior during the first hours inside the game. The prediction of the first purchase was then modeled as a multi-class classification problem. Across a range of different classification model families, the models trained on telemetric features of the present dataset all significantly outperform the same models trained on demographic features, which in turn outperform naive baselines. The result has implications for industry use-cases where Recommender Systems are being employed, and telemetric features can be aggregated, like mobile applications. It also has implications on future research of cold-start mitigation, as telemetric information could be used to generate recommendations in different problem architectures than classification. / Rekommendationssystem är informationsfiltreringssystem som försöker att förutsäga en användares preferens för en artikel. En viktig utmaning när man bygger ett rekommendationssystem är kallstarten, integrationen av nya användare i rekommendationsprocessen. Kallstarten kan fortfarande inte lösas fullständigt. Problemet blir nuförtiden mildrad genom att använda sig av externa datakällor om användaren. Detta arbete föreslår att man använder telemetrisk användningsdata för modellering av användarpreferens. Arbetet fokuserar på industriella användningsfallet av ett rekommendations system för artiklar inom ett mobilspel. Telemetriska egenskaper har konstruerats för att infånga spelarens beteende under de första timmarna i spelet. Rekommendationen för det relevantaste första köpet modellerades sedan som ett klassificeringsproblem med flera klasser. Över en rad olika klassificeringsmodellfamiljer överträffade modellerna tränade på telemetriska egenskaper signifikant samma modeller som tränats på demografiska egenskaper vilket i sin tur överträffar naiva basmetoder. Resultatet har konsekvenser för industriella applikationer där rekommendationssystem används och i vilka telemetriska egenskaper kan aggregeras, t.ex. mobilappar. Det har också konsekvenser för framtida forskning om kallstartreducering, eftersom telemetrisk information kan användas för att generera rekommendationer i andra problemklasser än klassificering.

Detta dokument presenterar implementeringen av ett rekommendationssystem för tjänstebokningssidan Boka.se. Rekommendationssystem omfattar mjukvaruverktyg och teknik för att generera förslag till en användare enligt deras preferenser och förekommer ofta på e-handelssidor. Baserat på användarens feedback kan det föreslagna rekommendationssystemet generera förslag för tjänster som passar dem. Det här dokumentet ger en översikt över rekommendationssystem och visar implementeringen av ett user-based collaborative filtering system, baserat på en data som tillhandahålls av tjänstbokningssidan Boka.se. Den beskriver också olika fallgropar och begränsningar för att skapa ett rekommendationssystem baserat på data som inte har några identifierande attribut för varken användare eller objekt.

Internet Protocol television (IPTV) is a way of delivering television over the Internet, which enables two-way communication between an operator and its users. By using IPTV, users have freedom to choose what content they want to consume and when they want to consume it. For example, users are able to watch TV shows after they have been aired on TV, and they can access content that is not part of any linear TV broadcasts, e.g. movies that are available to rent. This means that, by using IPTV, users can get access to more video content than is possible with the traditional TV distribution formats. However, having more options also means that deciding what to watch becomes more difficult, and it is important that IPTV providers facilitate the process of finding interesting content so that the users find value in using their services. In this thesis, the author investigated how a user’s online social network can be used as a basis for facilitating the discovery of interesting movies in an IPTV environment. The study consisted of two parts, a theoretical and a practical. In the theoretical part, a literature study was carried out in order to obtain knowledge about different recommender system strategies. In addition to the literature study, a number of online social network platforms were identified and empirically studied in order to gain knowledge about what data is possible to gather from them, and how the data can be gathered. In the practical part, a prototype content discovery system, which made use of the gathered data, was designed and built. This was done in order to uncover difficulties that exist with implementing such a system. The study shows that, while it is is possible to gather data from different online social networks, not all of them offer data in a form that is easy to make use of in a content discovery system. Out of the investigated online social networks, Facebook was found to offer data that is the easiest to gather and make use of. The biggest obstacle, from a technical point of view, was found to be the matching of movie titles gathered from the online social network with the movie titles in the database of the IPTV service provider; one reason for this is that movies can have titles in different languages. / Internet Protocol television (IPTV) är ett sätt att leverera tv via Internet, vilket möjliggör tvåvägskommunikation mellan en operatör och dess användare. Genom att använda IPTV har användare friheten att välja vilket innehåll de vill konsumera och när de vill konsumera det. Användare har t.ex. möjlighet att titta på tv program efter att de har sänts på tv, och de kan komma åt innehåll som inte är en del av någon linjär tv-sändning, t.ex. filmer som är tillgängliga att hyra. Detta betyder att användare, genom att använda IPTV, kan få tillgång till mer videoinnhåll än vad som är möjligt med traditionella tv-distributionsformat. Att ha fler valmöjligheter innebär dock även att det blir svårare att bestämma sig för vad man ska titta på, och det är viktigt att IPTV-leverantörer underlättar processen att hitta intressant innehåll så att användarna finner värde i att använda deras tjänster. I detta exjobb undersökte författaren hur en användares sociala nätverk på Internet kan användas som grund för att underlätta upptäckandet av intressanta filmer i en IPTV miljö. Undersökningen bestod av två delar, en teoretisk och en praktisk. I den teoretiska delen genomfördes en litteraturstudie för att få kunskap om olika rekommendationssystemsstrategier. Utöver litteraturstudien identifierades ett antal sociala nätverk på Internet som studerades empiriskt för att få kunskap om vilken data som är möjlig att hämta in från dem och hur datan kan inhämtas. I den praktiska delen utformades och byggdes en prototyp av ett s.k. content discovery system (“system för att upptäcka innehåll”), som använde sig av den insamlade datan. Detta gjordes för att exponera svårigheter som finns med att implementera ett sådant system. Studien visar att, även om det är möjligt att samla in data från olika sociala nätverk på Internet så erbjuder inte alla data i en form som är lätt att använda i ett content discovery system. Av de undersökta sociala nätverkstjänsterna visade det sig att Facebook erbjuder data som är lättast att samla in och använda. Det största hindret, ur ett tekniskt perspektiv, visade sig vara matchningen av filmtitlar som inhämtats från den sociala nätverkstjänsten med filmtitlarna i IPTV-leverantörens databas; en anledning till detta är att filmer kan ha titlar på olika språk.

對於大部分的人而言，寫作是一種深度的表達過程，需要詳盡而深入的描述能力及精準而嚴謹的語意呈現。對於非英語為母語的學習者(ESL/EFL)來說，英語寫作尤其是一個困難的過程，常常會因為單字、搭配字(collocation)、句型結構等方面的認知不足，或是受到母語認知的牽制影響，而造成用詞、語法、甚至語意的錯誤。 近年來，語料庫的發展帶來了豐富的語言資源，不僅可以對語言的使用提供許多統計分析上的資訊，也可以做為語言學習者在特定詞語使用上的參照對象。目前語料庫的詞語使用參照以concordance技術為主，提供以特定字詞為基準的上下文例句排列，這種功能對ESL/EFL學習者於寫作過程所提供的參照仍然相當有限；而對於不同的查詢條件，若沒有良善的輸入和比對機制，往往搜尋上的數量和例句品質無法滿足學習者的參照需求；除此之外，對於檢索結果語料庫也沒有評估與排序的概念，學習者往往需要在大量的資訊中篩選出有用的資訊；綜觀以上幾點，目前語料庫技術無法滿足不同程度的ESL/EFL寫作者在參照協助上的需求。 本研究提出一種文句推薦方法，針對ESL/EFL寫作者在寫作過程上的認知不足或不確定，從語料庫中尋找出可用的參照例句，進而提供針對性的協助。我們的文句推薦方法包含三個模組，第一模組是一個彈性的表達元素模組，能針對寫作過程中的語言資訊需求，以規定的表達元素呈現作者的認知需求。第二模組是一個檢索模組，指從語料庫中比對尋找符合作者語意需求的例句。第三模組是一個排序的模組，針對找出的例句，評估其符合作者語意需求的程度，並將之排序，以提升例句參照的使用效率。 本研究依據文句推薦方法實做雛形系統，以British National Corpus和科學人雜誌語料庫(Science America)當例句來源，並依據論文的客觀評估和學習者的問卷調查兩種評估方式來評量雛形系統的成效。經由上述兩種評估方式，本研究驗證雛形系統能針對ESL/EFL於寫作過程中給予一定程度的協助成效，證明本研究的文句推薦方法確實能達成寫作協助的既定目標。 / For most people, writing is a process of profundity. It requires well description of capabilities and excellent semantic of representations. For ESL/EFL (English as a Second Language/English as a Foreign Language) learners, English writing is a particularly difficult process. They have error in words, syntax or semantics because their expressions are constrained by their mother tongue or by lacking of the awareness of vocabularies, collocation, or sentence-structures. In recent years, according to the development of corpus technique, we have rich resource in language. The resource can not only provide many statistical analyses of informations in language research but also can be used as a reference in the use of specific words for the learners. Current corpus technique use concordance which displays the words with their surrounding text to present sentences, but that approach provides very limited references in the writing process for ESL/EFL learners. Also considering the different inquired queries, if we have no well-defined input and retrieval module, the quantity and quality of results could not meet the demand for the learners. In addition, corpus technique doesn’t provide the ranking and evaluating skills to the retrieval results. As a result of that, learners often require filtering a large number of information to find something useful. In view of the above points, current corpus technique is unable to satisfy different degrees of ESL/EFL learners in the writing process. This paper presents a Sentence Recommendation Approach technique. With regard to the inadequate knowledge in the writing process for ESL/EFL learners, we search for useful sentences from the corpus and provide them to learners. Our Sentence Recommendation Approach technique has three modules: one is Expression Element Module. It allows the learners to use some expression elements to represent their demand. Another is Retrieval Module. It means the process of searching the corresponding sentences based on the expression elements in Expression Element Module. The last is Sort Module. It means the process of ranking the results derived from the Retrieval Module. Our research establishes the experimental system to verify the performance of our Sentence Recommendation Approach technique. We use British National Corpus and Science America Corpus for the source of sentences. The evaluation is divided into our objective evaluation and the questionnaire evaluation. Both of them prove that our experimental system does some favor in the writing process for ESL/EFL learners. In other words, our Sentence Recommendation Approach technique really helps the learners in the writing process.

Modelos baseados em fatoração de matrizes estão entre as implementações mais bem sucedidas de Sistemas de Recomendação. Neste projeto, estudamos as possibilidades de incorporação de informações provindas de redes sociais, para melhorar a qualidade das predições do modelo tanto em modelos tradicionais de Filtragem Colaborativa, quanto em Filtragem Colaborativa Neural. / Models based on matrix factorization are among the most successful implementations of Recommender Systems. In this project, we study the possibilities of incorporating the information from social networks to improve the quality of predictions of the model both in traditional Collaborative Filtering and in Neural Collaborative Filtering.

A presente pesquisa analisou a recomendação de objetos de aprendizagem (OAs) por competências, a partir da colaboração baseada na avaliação pela qualidade percebida dos objetos de aprendizagem utilizados, considerando os estados de ânimo dos alunos. Neste sentido, a pesquisa visou desenvolver e validar um sistema de recomendação por qualidade percebida de objetos de aprendizagem por competência, que considera o estado de ânimo do aluno, denominado Requali. Este agregou os estados de ânimo dos usuários e a avaliação de qualidade percebida ao processo de recomendação de objetos de aprendizagem por competências. Os sistemas de recomendação integram processos que permitem caracterizar o perfil do usuário, as características do objeto e a avaliação do que está sendo disponibilizado. Assim, a fundamentação teórica inclui a recomendação de objetos de aprendizagem; os estados de ânimo dos usuários; e a avaliação de qualidade percebida. Para a recomendação de OAs, adotaram-se os estudos de Cazella, Nunes e Reategui (2010) sobre a relevância e as características dos sistemas de recomendação; e de Cazella et al. (2012) que tratam do RecOAComp como sistema de recomendação de objetos de aprendizagem por competências. Acerca dos estados de ânimo, incluíram-se o trabalho de Bercht (2001), sobre a essencialidade da afetividade no processo de ensino; e o de Longhi (2011), sobre o mapa afetivo como funcionalidade do Ambiente Virtual de Aprendizagem ROODA (Rede Cooperativa de Aprendizagem) para reconhecimento dos estados de ânimo. Por fim, utilizou-se a teoria da qualidade percebida desenvolvida por Oliver (1997), que trata das expectativas, das percepções e da avaliação da qualidade percebida pelos sujeitos. A pesquisa possibilitou a construção da estrutura de uma Matriz que foi utilizada posteriormente no Requali. Os critérios utilizados (expectativa, percepção e qualidade percebida) foram identificados na literatura e discutidos por um grupo focal de professores de Administração. O sistema Requali foi desenvolvido com tecnologia PHP, CSS e banco de dados MySQL. Ele foi validado por meio de um curso de extensão oferecido para alunos de graduação de Administração. Os alunos apontaram que o sistema fornece disponibilizações adequadas. Entretanto destacaram limitações de uso referentes ao reconhecimento de conceitos presentes na interface do sistema e na recuperação de informações, apesar de terem caracterizado o Requali como atrativo, fácil de usar e motivador. Assim, constatou-se que o Requali ao associar a avaliação pela qualidade percebida, baseada na Matriz Requali, e os estados de ânimos dos alunos para a recomendação de OAs por competências oferece itens disponibilizados que se aproximam dos interesses dos alunos. / This research analyzed the recommendation of learning objects (LO) by competence from the contribution based on the perceived quality evaluation of the learning objects that have been used, considering students’ state of mood. In this sense, the research aimed at developing and validating a recommendation system by perceived quality of learning objects, which considers students’ state of mood, that is named Requali. This system has associated users’ states of mood and perceived evaluation in the recommendation process of learning objects by competencies. Recommender systems integrate processes that allow the characterization of the user’s profile, the object’s characteristics and the evaluation of what is made available. Thus the theoretical framework includes learning objects recommendation; users’ states of mood; and the assessment of the perceived quality. For LO recommendation, we adopted the studies of Cazella, Nunes and Reategui (2010) on the relevance and characteristics of recommender systems; and the studies of Cazella et al. (2012) that approaches the RecOAComp as a learning objects recommender system by competencies. Regarding states of mood, we included the work of Bercht (2001) on the essentiality of affectivity in the teaching process; and Longhi’s work (2011) on the emotional map as a functionality of the ROODA (Learning Cooperative Network) Learning Virtual Environment for states of mood recognition. Finally, we used the perceived quality theory developed by Oliver (1997), which approaches the subject’s expectations, perceptions and assessment of the perceived quality. The research enabled the construction of the structure of a matrix which was subsequently used in Requali. The used criteria (expectation, perception and perceived quality) were identified in the literature and discussed by a focus group with business administration professors. Requali system was developed with PHP, CSS and MySQL database. It has been validated by an extension course offered for Business Administration undergraduate students. The students have pointed out that the system provides appropriate recommendations. However, they have highlighted limitations of use regarding concepts recognition in the system’s interface and information retrieval, although they have also characterized Requali as attractive, easy to use and motivating. Thus it was found that Requali, associating perceived quality evaluation based on the Requali Matrix with the students’ states of mood for LO recommendation by competencies,offers available items that are close to student’s interests. / Esta investigación examinó las recomendaciones de los objetos de aprendizaje de competencias (los) de la colaboración basada en la evaluación de la calidad percibida de los objetos utilizados aprendizaje, teniendo en cuenta los estados de ánimo de los estudiantes. En este sentido, la investigación tuvo como objetivo desarrollar y validar un sistema de recomendación de calidad para la competencia percibida por los objetos de aprendizaje, de los objetos de aprendizaje (OAs) por competencia, que considera el estado de ánimo del estudiante llamado Requali. Este añadió los estados de ánimo de los usuarios y la evaluación de la calidad percibida en el proceso de recomendación de los objetos de aprendizaje por competencias. Los sistemas de recomendación se integran procesos que permiten caracterizar el perfil de lo usuario, las características del objeto y la evaluación de lo que se está poniendo a disposición. Así, el marco teórico incluye la recomendación de objetos de aprendizaje; estados de ánimo de los usuários; y la evaluación de la calidad percibida. Hacia la recomendación de los OAs, fueron adoptdos los estudios de Cazella, Nunes y Reátegui (2010) acerca de la importancia y las características de los sistemas de recomendación; y Cazella et al. (2012), relativo a ló RecOAComp como sistema de recomendación de objetos de aprendizaje por competencias. En cuanto a los estados de ánimo, se incluye el trabajo de Bercht (2001) sobre la esencialidad de la afectividad en el proceso de enseñanza; y Longhi (2011), sobre el mapa afectivo como la funcionalidad del Entorno Virtual de Aprendizaje ROODA (Red de Aprendizaje Cooperativa) para el reconocimiento de los estados de ánimo. Por último, se utilizó la teoría de la calidad percibida desarrollada por Oliver (1997), que se ocupa de las expectativas, las percepciones y la evaluación de la calidad percibida por los sujetos. La investigación permitió la construcción de la estructura de una matriz que fuera utilizada posteriormente en ló Requali. Los critérios utilizados (expectativa, la percepción y la calidad percibida) fueron identificados en la literatura y discutidos por um grupo focal de profesores de Gestión. El sistema Requali fue desarrollado con tecnología PHP, CSS y banco de datos MySQL. Fue validado por un curso de extensión ofrecido para estudiantes universitarios de Administración. Los estudiantes señalaron que el sistema proporciona disponibilizaciones apropiadas. Sin embargo han resaltado las limitaciones de uso para el reconocimiento de los conceptos presentes en la interfaz del sistema y la recuperación de información a pesar de que caracterizarán lo Requali como atractivo, fácil de usar y motivador. Por lo tanto, se encontró que el Requali asociando la evaluación por parte de la calidad percibida en base a la matriz Requali, y los estados de ánimo de los estudiantes para la recomendación de OAs por la competencia ofrece los elementos disponibles que se cercan de los intereses de los estudiantes.