Social Media Streams allow users to share user-generated content as well as aggregate different streams into one single stream. Additional Enterprise Social Media Streams organize the stream messages into projects with different usage patterns compared to public collaboration platforms such as Twitter. The aggregated stream helps the user to access the information in one single place but also leads to an information overload. Here, a recommendation engine can help to distinguish between relevant and irrelevant information for the users. In previous work we showed how features inferred from messages can predict relevant information and can be used to learn a user model. In this paper we show how this approach can be used in a productive enterprise social media stream application without using explicit user feedback. We develop a time binned evaluation measure which suits the scenario to steadily recommend messages of the stream. Finally, we evaluate our algorithm in different variations and show that it helps to identify relevant messages.

La Web Social y la Web Semántica han impactado en la forma en que la creación de conocimiento se ha llevado a cabo en la Web. La Web Social promociona la participación de los usuarios para crear y editar contenido y conocimiento en la Web. La proliferación de contenido y la necesidad de tener una administración automatizada de esta información disparó la aparición de la Web Semántica. Actualmente, la Web Social y la Web Semántica conviven y comparten un mismo tema: un mejor manejo del conocimiento. Sin embargo, la mayoría de la información en la Web Social no es parte de la Web Semántica, y la información de la Web Semántica no es utilizada para mejorar a la Web Social. Esta tesis presenta un enfoque innovador para estimular una co-evolución entre la Web Semántica y la Web Social: las fuerzas que impulsan la Web Social y las herramientas que llevan a cabo la Web Semántica trabajando en conjunto con el fin de tener beneficios mutuos. En este trabajo afirmamos que la co-evolución entre la Web Social y la Web Semántica mejorará la generación de información semántica en la Web Semántica, y mejorará la producción de conocimiento en la Web Social. Esto invita a responder las siguientes preguntas: ¿Cómo puede incluirse la generación de datos semánticos en las actividades de los usuarios de la Web Social? ¿Como puede definirse la semántica de un recurso web en un entorno social? ¿Cómo puede inyectarse en la Web Social las nuevas piezas de información extraídas de la Web Semántica? ¿Poseen las comunidades de la Web Social convenciones generales que deban ser respetadas? Con el fin de mejorar la Web Semántica con las fuerzas de la Web Social, en este trabajo se proponen dos enfoques de Social Semantic Tagging: P-Swooki que permite a usuarios de una wiki semántica gestionar anotaciones semánticas permitiendo completar el proceso de construcción de conocimiento, y Semdrops que permite a los usuarios describir en forma semántica cualquier recurso de la Web tanto en un espacio de conocimiento personal como en un espacio compartido. Además, con el fin de mejorar el contenido de la Web Social, proponemos BlueFinder: un sistema de recomendación que detecta y recomienda la mejor manera de representar en un sitio de la Web Social, información que es extraída de la Web Semántica. En particular, BlueFinder recomienda la manera de representar una propiedad semántica de DBpedia en Wikipedia, respetando las convenciones de la comunidad de usuarios de Wikipedia. / Tesis realizada en co-tutela con la Universidad de Nantes (Francia). Director de tesis por la Universidad de Nantes: Pascal Molli; co-director de tesis por la Universidad de Nantes: Hala Skaf-Molli. Grado alcanzado por la Universidad de Nantes: Docteur de l'Université de Nantes.

近年來，智慧型手機(Smartphone)的銷量超過其他型式手機。智慧型手機具有更先進、更開放的行動作業系統，可允許使用者自行安裝應用程式軟體(Application)來擴充手機功能。目前市面上的應用程式數量非常龐大，在眾多的應用程式和有限的時間下，使用者不太可能將所有的應用程式下載試用，所以對使用者而言，找出自己所想要和需要的應用程式，是個困難的問題。推薦系統可依照使用者的喜好，或是準備推薦項目的相似程度來做推薦，讓使用者能較快得到想要的資訊，目前主要的方式有協同過濾(Collaborative Filtering, CF)、內容過濾(Content-Based Filtering, CBF)，還有結合前述兩種方式的混和式推薦(Hybrid Approach)。 本研究所使用的資料集是由政治大學資訊科學系所開發的實驗平台蒐集而來。資料以側錄的方式，將使用者實際操作手機應用程式的狀況記錄下來，其中包含了25位使用者和1125個應用程式。我們將原始資料集以三種方式整理成三個資料集：一、是否使用應用程式；二、使用應用程式的次數；三、使用應用程式的頻率，其值表示使用者在該應用程式的使用狀況。我們並將資料分成前段與後段時間兩部分，以前段時間的資料當作基準，推薦最多同群使用者使用的應用程式、同群使用者使用次數最多的應用程式，以及同群使用者最常使用的應用程式，然後以後段時間的資料做驗證，計算推薦結果的準確率與召回率加以比較。 我們使用知名的Information Theoretic Co-Clustering Algorithm和兩種基於Minimum Squared Residue Co-Clustering Algorithm的演算法將使用者與應用程式分群，利用分群結果做計算，推薦應用程式給使用者。實驗發現三種演算法在第一個資料集的準確率與召回率表現最好，此資料集以0和1的值，來紀錄使用者在各應用程式的使用狀況。實驗比較三個演算法的結果，在大部分的情況之下，一個基於Minimum Squared Residue Co-Clustering Algorithm的演算法，給出的結果較好。 此外，我們也發現應用程式開發者將應用程式上架提供下載時，以個人主觀想法對該應用程式定義其分類，與我們利用雙分群方法，以使用者實際操作的情況將應用程式分類的結果有些差異，或許在Google Play的分類上可做調整。 本研究提出推薦系統的框架具有彈性，未來可以使用不同的雙分群演算法做分群，也能套用其他的推薦方式。 / With the rapid evolution of smartphone devices, tens of thousands applications have been supplied on online stores such as App Store (operated by Apple Inc.) and Google Play (operated by Google Inc.). Since there are many applications, recommending applications to users becomes an important topic. In this thesis, we present a framework for using a co-clustering algorithm to recommend applications to users. Recommendations are a part of everyday life. People usually rely on some external knowledge to make informed decisions about a particular artifact or action. Using recommender systems is one of general approaches that help people make decisions. There are three common types of recommender systems, namely collaborative filtering, content-based filtering, and hybrid recommender systems. In this thesis, we use the dataset that was collected by a tool developed by the Department of Computer Science at the National Chengchi University. It recorded the users’ behavior when they were using their smartphones. We transform the original dataset into three types of datasets: 1) indicating whether a user used an application; 2) indicating the number of uses made by a user for an application; 3) indicating the frequency of uses made by a user for an application. Furthermore, we divide each dataset into two parts: The first part containing data for the early time period is used as the recommending base, and the second part containing data for the late time period is used for verifying the results. We utilize three famous co-clustering algorithms, which are the Information Theoretic Co-Clustering Algorithm and two algorithms based on the Minimum Squared Residue Co-Clustering Algorithm, in the proposed framework. According to the clusters given by a co-clustering algorithm, we recommend top five applications to each user by referring to the maximum number of users, the maximum number of uses, and the most frequently used applications that are in the same cluster. We calculate the precision and recall values to compare the results. From the experimental results, we find that the best result corresponds to the first type of dataset and also that one of the algorithms based on the Minimum Squared Residue Co-Clustering Algorithm is better than the other two algorithms in terms of the precision and recall values. From the clusters of applications, we obtain some interesting insights into the categories of applications. The categories of applications are set by their developers, but the users may not totally agree with the settings. There might be space for improvement for the categories of applications on the online store. In the future, we can utilize different co-clustering algorithms and other recommended methods in the proposed framework.

No description available.

Personalizuotos mokymosi aplinkos kūrimas parenkant besimokančiajam tinkamas internetines priemones yra sudėtingas ir aktualus šių dienų uždavinys. Dabartinis besimokantysis turėtų pats imtis iniciatyvos, būti atsakingas už mokymosi procesą, mokėti pasirinkti tinkamas mokymosi priemones, tačiau parama mokymosi metu irgi yra labai svarbi, nes abejotina, ar besimokančiojo savarankiškai pasirinktos priemonės padės įgyvendinti siekiamų mokymosi tikslų ir veiklų optimaliausiu būdu. Disertaciniame darbe išnagrinėti rekomendavimo sistemų ir jose naudojamų vartotojo profilių tipai, rekomendavimo būdai, šių sistemų taikymo galimybės personalizuotam mokymuisi, mokymosi proceso ir antrosios kartos saityno priemonių sąveika, pagrindinės savybės. Pateikiamas antrosios kartos saityno priemonių komponavimo mokymosi procese metodas parenkantis tam tikram besimokančiajam priemonę atsižvelgus į mokymosi tikslus, norimą įgyvendinti mokymosi veiklą, teikiamą pirmenybę mokymosi turiniui bei bendravimo formai. Nagrinėtoji dalykinė sritis aprašyta ontologijoje, o pasiūlyto metodo etapai įgyvendinti žiniomis grindžiamos rekomendavimo sistemos prototipe. Sukurtoji sistema rekomenduoja tam tikrą mokymosi stilių turinčiam besimokančiajam visas jos žinių bazėje esančias internetines priemones, kuriomis naudodamasis besimokantysis gali atlikti nurodytą mokymosi veiklą. Pasiūlytą metodą įvertino parinkti ekspertai – buvo įsitikinta metodo kokybe, t. y. tikslumu, tinkamumu ir našumu (laiko atžvilgiu)... [toliau žr. visą tekstą] / Although in practice and in many sources of literature the need for the application of Web 2.0 tools in education is highlighted, however there is lack of clear methods how these tools could be applied in learning for a higher learning quality, and there is a lack of e-learning systems that implement personalised Web 2.0 tools selection. In the work, personalised Web 2.0 tools selection method is presented. In the research, first of all, personalised e-learning technological peculiarities i.e. recommender systems applications for learning personalisation and those systems components were investigated. After that, selection methods for Web 2.0 tools suitable for implementing learning activities were analysed. The method of integrating Web 2.0 tools into personalised learning activities according to students learning styles was created (this method takes into account student’s learning preferences for content and communication modes tailored to the learning activities with a view to help the learner to quickly and accurately find the right educational tools) and prototype of the recommender system that implements the method proposed was developed. Finally, the expert evaluation of the developed system prototype that implements the method proposed was performed.

Although in practice and in many sources of literature the need for the application of Web 2.0 tools in education is highlighted, however there is lack of clear methods how these tools could be applied in learning for a higher learning quality, and there is a lack of e-learning systems that implement personalised Web 2.0 tools selection. In the work, personalised Web 2.0 tools selection method is presented. In the research, first of all, personalised e-learning technological peculiarities i.e. recommender systems applications for learning personalisation and those systems components were investigated. After that, selection methods for Web 2.0 tools suitable for implementing learning activities were analysed. The method of integrating Web 2.0 tools into personalised learning activities according to students learning styles was created (this method takes into account student’s learning preferences for content and communication modes tailored to the learning activities with a view to help the learner to quickly and accurately find the right educational tools) and prototype of the recommender system that implements the method proposed was developed. Finally, the expert evaluation of the developed system prototype that implements the method proposed was performed. / Personalizuotos mokymosi aplinkos kūrimas parenkant besimokančiajam tinkamas internetines priemones yra sudėtingas ir aktualus šių dienų uždavinys. Dabartinis besimokantysis turėtų pats imtis iniciatyvos, būti atsakingas už mokymosi procesą, mokėti pasirinkti tinkamas mokymosi priemones, tačiau parama mokymosi metu irgi yra labai svarbi, nes abejotina, ar besimokančiojo savarankiškai pasirinktos priemonės padės įgyvendinti siekiamų mokymosi tikslų ir veiklų optimaliausiu būdu. Disertaciniame darbe išnagrinėti rekomendavimo sistemų ir jose naudojamų vartotojo profilių tipai, rekomendavimo būdai, šių sistemų taikymo galimybės personalizuotam mokymuisi, mokymosi proceso ir antrosios kartos saityno priemonių sąveika, pagrindinės savybės. Pateikiamas antrosios kartos saityno priemonių komponavimo mokymosi procese metodas parenkantis tam tikram besimokančiajam priemonę atsižvelgus į mokymosi tikslus, norimą įgyvendinti mokymosi veiklą, teikiamą pirmenybę mokymosi turiniui bei bendravimo formai. Nagrinėtoji dalykinė sritis aprašyta ontologijoje, o pasiūlyto metodo etapai įgyvendinti žiniomis grindžiamos rekomendavimo sistemos prototipe. Sukurtoji sistema rekomenduoja tam tikrą mokymosi stilių turinčiam besimokančiajam visas jos žinių bazėje esančias internetines priemones, kuriomis naudodamasis besimokantysis gali atlikti nurodytą mokymosi veiklą. Pasiūlytą metodą įvertino parinkti ekspertai – buvo įsitikinta metodo kokybe, t. y. tikslumu, tinkamumu ir našumu (laiko atžvilgiu)... [toliau žr. visą tekstą]

No description available.

The growth of Web technologies has benefited researchers and the academic community by supporting the access of electronic publications as soon as they have been finished and published. In this context, Digital Libraries emerges as complex information systems which are essential for disseminating and preserving data, information and knowledge. However, due to the high amount of content available on the Web, specially in Digital Libraries, users face many correlated options, what result in the phenomenon known as information overload. Aiming to decrease or even eliminate these diffculties, recommender systems for Digital Libraries have been proposed and developed. This work presents a personalized recommender system which presents alternative ways to achieve better query results. For this, the main existing approaches of automatic recommendation have been studied in order to identify extension points and points to be improved. The proposed recommender system follows a hybrid approach which combines filtering techniques, content-based recommendation and collaborative recommendation. A hybrid recommendation engine has been proposed, which uses standard technologies for content description (Dublin Core), for communication with Digital Libraries (OAI-PMH Protocol ), as well as the user profile extracted from the curriculum vitae Lattes. The proposed solution has been evaluated in the context of the CiteSeer database, which contains papers and articles in the Dublin Core format. The preliminary results has showed an improvement in the quality of recommendation, thus presenting a better precision and coverage, when compared with existing approaches based either on content-based recommendation or on collaborative recommendation / O crescimento acelerado das tecnologias Web tem beneficiado pesquisadores e acadêmicos, pois as publicações de pesquisa podem ser acessadas eletronicamente tão logo elas tenham sido finalizadas e publicadas. Nesse contexto, surgem as Bibliotecas Digitais como um sistema de informação complexo que possui uma série de atividades que integram coleções, serviços e pessoas em suporte ao completo ciclo de criação, disseminação, acesso e preservação de dados, informação e conhecimento. No entanto, devido a enorme quantidade de conteúdo presente na Web, em particular nas Bibliotecas Digitais, usuários acabam se deparando com uma diversidade muito grande de opções, o que leva ao fenômeno conhecido como sobrecarga de informação. Com o objetivo de contribuir para amenizar ou até mesmo eliminar essas dificuldades, sistemas de recomendação para Bibliotecas Digitais têm sido propostos e desenvolvidos. Este trabalho segue essa direção, investigando soluções alternativas para alcançar mais qualidade nas indicações geradas por um sistema de recomendação na sua tarefa de ajudar os seus usuários. Para isso estudou-se as abordagens tratadas na literatura especializada sobre tais sistemas, propondo-se em seguida, um sistema de recomendação personalizada de artigos científicos para Bibliotecas Digitais. Tal sistema seguiu uma abordagem híbrida, procurando tirar proveito das características interessantes identificadas nas técnicas de filtragem e recomendação baseadas em conteúdo e colaborativa. Nesse sentido desenvolveu-se um engenho de recomendação híbrido que se utiliza de tecnologias padrão para a descrição de conteúdo (Padrão Dublin Core), comunicação com Bibliotecas Digitais (Protocolo OAI-PMH) e perfil do pesquisador (Currículo Lattes). Finalmente, avaliou-se o sistema proposto sobre uma base de dados do CiteSeer contendo artigos no formato Dublin Core, tendo os resultados preliminares mostrado-se satisfatórios melhorando a precisão na recomendação e a cobertura quando comparado com sistemas que implementam abordagens baseada em conteúdo e colaborativa isoladamente

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.

Au cours des dernières années, le Web a connu une énorme croissance en matière de contenus et d'utilisateurs. Ce phénomène a entraîné des problèmes liés à la surcharge d'information face à laquelle les utilisateurs ont des difficultés à trouver les bonnes informations. Des systèmes de recommandation ont été développés pour résoudre ce problème afin de guider les utilisateurs dans ce flux d'informations. Les approches de recommandation se sont multipliées et ont été mises en œuvre avec succès, notamment au travers d’approches telles que le filtrage collaboratif. Cependant, il existe encore des défis et des limites qui offrent des opportunités pour de nouvelles recherches. Parmi ces défis, la conception de systèmes de recommandation de lectures est devenue un axe de recherche en pleine expansion suite à l’apparition des bibliothèques numériques.Traditionnellement, les bibliothèques jouent un rôle passif dans l’interaction avec les lecteurs et ce, faute d’outils efficaces de recherche et de recommandation. Dans ce manuscrit, nous nous sommes penchée sur la création d’un système de recommandation de lectures. Nos objectifs portent sur :- améliorer la compréhension des besoins utilisateurs exprimés au sein des requêtes en langage naturel de recherches de livres, articles et billets ; - pallier l'absence de liens explicites entre ouvrages et articles de revues par la détection et l'analyse automatique des références bibliographiques afin de proposer des liens ; - parvenir à un système de recommandation de lectures s'appuyant sur des données textuelles permettant de fournir une liste de recommandations personnalisées aux utilisateurs actifs. / In the recent years, the Web has undergone a tremendous growth regarding both content and users. This has led to an information overload problem in which people are finding it increasingly difficult to locate the right information at the right time. Recommender systems have been developed to address this problem, by guiding users through the big ocean of information. The recommendation approaches have multiplied and have been successfully implemented, particularly through approaches such as collaborative filtering. However, there are still challenges and limitations that offer opportunities for new research. Among these challenges, the design of reading recommendation systems has become a new expanding research focus following the emergence of digital libraries.Traditionally, libraries play a passive role in interaction with users due to the lack of effective search and recommendation tools. In this manuscript, we will study the creation of a reading recommendation system in which we'll try to exploit the possibilities of digital access to scientific information. Our objectives are: - to improve the understanding of user needs expressed in natural language search queries for books, articles and posts. This work will require the establishment of processes capable of exploiting the structures of data and their dimension; - to compensate for the absence of explicit links between books and journal articles by automatically detecting and analyzing bibliographic references, and then to propose links;- to achieve a reading recommendation system based on textual data to provide a customized recommendation list to active users, similar to systems already used by users profiles.