The evolution of the Internet has brought us into a world that represents a huge amount of information items such as music, movies, books, web pages, etc. with varying quality. As a result of this huge universe of items, people get confused and the question &ldquo / Which one should I choose?&rdquo / arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present information items that are likely of interest to the user. A variety of information filtering techniques have been proposed for performing recommendations, including content-based and collaborative techniques which are the most commonly used approaches in recommendation systems. This thesis work introduces ReMovender, a content-based movie recommendation system which is empowered by collaborative missing data prediction. The distinctive point of this study lies in the methodology used to correlate the users in the system with one another and the usage of the content information of movies. ReMovender makes it possible for the users to rate movies in a scale from one to five. By using these ratings, it finds similarities among the users in a collaborative manner to predict the missing ratings data. As for the content-based part, a set of movie features are used in order to correlate the movies and produce recommendations for the users.

在西洋棋中，開局決定了棋局未來發展的基礎，棋手在開局階段局勢的好壞，會直接影響到接下來中局的發展，乃至全局的勝負。隨著西洋棋的演進，棋手們在比賽中進行各式各樣的棋步嘗試，發展出眾多經歷實戰考驗的開局，目前西洋棋的開局多達上千種變化，使得棋手在學習西洋棋的過程中，要花上大量的時間從眾多的西洋棋開局變化中，尋找適合自己的開局鑽研與使用。為幫助棋手在此階段的學習，本論文提出西洋棋開局推薦系統，從大數據協助學習的觀點，利用大量棋手們的開局經驗，對棋手做個人化的開局推薦。此系統以風格、棋力相似的棋手們所選用的開局為推薦基礎，並考量棋手習慣使用的下棋模式，推薦棋手善於發揮自身優勢、易於理解，並且投其所好的開局。為此，此西洋棋開局推薦系統包含風格分析、棋力評估、棋形截取，以及混合式推薦等部分。依據棋手過去的對局記錄，風格分析評估棋手下棋偏好冒險或保守的程度；棋力評估將傳統西洋棋棋力轉成可直觀比較棋手棋力程度差異之量表；棋形截取找出棋手習慣使用的下棋模式。最後，混合式推薦綜合考量上述三項因素，推薦出符合棋手棋風、棋力與下棋習慣模式的開局。 本論文以兩個實驗來評估風格分析與開局推薦系統的效果，在風格分析的實驗中，將風格分析方法評估棋手風格的結果與專家判斷的結果做比較；在開局推薦系統的實驗中，以棋手是否將會在比賽使用系統所推薦的開局來評估推薦效果。實驗結果顯示，風格分析對於世界冠軍棋手的風格評估幾乎與專家的判斷相同；開局推薦系統針對開局所設計的混合式推薦方法，推薦效果優於常見的推薦方法。 / The Opening is the fundamental phase of a chess game, and significantly affects the result of a competition. With the evolution of chess, there has been developed thousands of chess openings at present. This makes it difficult and time-consuming for chess players to find and learn the openings suitable for them. For helping players to learn chess in the opening, we provide Opening Recommendation System (OPRS), which considers chess players’ experiences and recommends chess openings that could be understandable and favorite for the players. For personalized recommendation, OPRS analyzes the playing style, translates chess rating, extracts the playing patterns, and then performs hybrid recommendation based on the features obtained. In the evaluation, the performance of the playing style analysis are demonstrated by comparing with the styles judged by chess experts for world chess championships. For OPRS, the evaluations are according to the openings the players use in the chess tournaments in the next years. The experiments show that OPRS achieves good accuracies of the playing style analysis and outperforms the competitive methods for chess opening recommendation.

Distributed systems like e-commerce and e-market places, peer-to-peer networks, social networks, and mobile ad hoc networks require cooperation among the participating entities to guarantee the formation and sustained existence of network services. The reliability of interactions among anonymous entities is a significant issue in such environments. The distributed entities establish connections to interact with others, which may include selfish and misbehaving entities and result in bad experiences. Therefore, trustworthiness evaluation using trust management techniques has become a significant issue in securing these environments to allow entities decide on the reliability and trustworthiness of other entities, besides it helps coping with defection problems and stimulating entities to cooperate. Recent models on evaluating trustworthiness in distributed systems have heavily focused on assessing trustworthiness of entities and isolate misbehaviours based on single trust metrics. Less effort has been put on the investigation of the subjective nature and differences in the way trustworthiness is perceived to produce a composite multidimensional trust metrics to overcome the limitation of considering single trust metric. In the light of this context, this thesis concerns the evaluation of entities’ trustworthiness by the design and investigation of trust metrics that are computed using multiple properties of trust and considering environment. Based on the concept of probabilistic theory of trust management technique, this thesis models trust systems and designs cooperation techniques to evaluate trustworthiness in mobile ad hoc networks (MANETs). A recommendation based trust model with multi-parameters filtering algorithm, and multidimensional metric based on social and QoS trust model are proposed to secure MANETs. Effectiveness of each of these models in evaluating trustworthiness and discovering misbehaving nodes prior to interactions, as well as their influence on the network performance has been investigated. The results of investigating both the trustworthiness evaluation and the network performance are promising.

L'accès aux informations pertinentes, adaptées aux besoins et au profil de l'utilisateur est un enjeu majeur dans le cadre actuel caractérisé par une prolifération massive des ressources d'information hétérogènes. Le développement d'appareils mobiles équipés de plusieurs fonctionnalités telles que la caméra, le WIFI, la géo-localisation et bien plus d'autres permettent aux systèmes mobiles de recommandation actuels d'être hautement contextualisés et pouvant fournir à l'utilisateur des informations pertinentes au bon moment quand il en a le plus besoin, sans attendre qu'il établisse une interaction avec son appareil. C'est dans ce cadre que s'insère notre travail de thèse. En effet, nous proposons une approche de recommandation contextuelle et proactive dans un environnement mobile qui permet de recommander des informations pertinentes à l'utilisateur sans attendre à ce que ce dernier initie une interaction. Un système proactif peut prendre la forme d'un guide touristique personnalisé qui se base sur la localisation et les préférences de l'utilisateur pour suggérer à ce dernier des endroits intéressants sans qu'il fournisse, sa préférence ou une requête explicite. Cela permettra de réduire les efforts, le temps et l'interaction de l'utilisateur avec son appareil mobile et de présenter les informations pertinentes au bon moment et au bon endroit. Cette approche prend aussi en considération les situations où la recommandation pourrait déranger l'utilisateur. Il s'agit d'équilibrer le processus de recommandation contre les interruptions intrusives. En effet, il existe différents facteurs et situations qui rendent l'utilisateur moins ouvert aux recommandations. Comme nous travaillons dans le contexte des appareils mobiles, nous considérons que les applications mobiles telles que la caméra, le clavier, l'agenda, etc., sont de bons représentants de l'interaction de l'utilisateur avec son appareil puisqu'ils représentent en quelque sorte la plupart des activités qu'un utilisateur pourrait entreprendre avec son appareil mobile au quotidien, comme envoyer des messages, converser, tweeter, naviguer ou prendre des photos. / Just-In-Time recommender systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user's interests at the right time. Our work falls within this framework and focuses on developing a proactive context-aware recommendation approach for mobile devices that covers many domains. It aims at recommending relevant items that match users' personal interests at the right time without waiting for the users to initiate any interaction. Indeed, the development of mobile devices equipped with persistent data connections, geolocation, cameras and wireless capabilities allows current context-aware recommender systems (CARS) to be highly contextualized and proactive. We also take into consideration to which degree the recommendation might disturb the user. It is about balancing the process of recommendation against intrusive interruptions. As a matter of fact, there are different factors and situations that make the user less open to recommendations. As we are working within the context of mobile devices, we consider that mobile applications functionalities such as the camera, the keyboard, the agenda, etc., are good representatives of the user's interaction with his device since they somehow stand for most of the activities that a user could use in a mobile device in a daily basis such as texting messages, chatting, tweeting, browsing or taking selfies and pictures.

Distributed systems like e-commerce and e-market places, peer-to-peer networks, social networks, and mobile ad hoc networks require cooperation among the participating entities to guarantee the formation and sustained existence of network services. The reliability of interactions among anonymous entities is a significant issue in such environments. The distributed entities establish connections to interact with others, which may include selfish and misbehaving entities and result in bad experiences. Therefore, trustworthiness evaluation using trust management techniques has become a significant issue in securing these environments to allow entities decide on the reliability and trustworthiness of other entities, besides it helps coping with defection problems and stimulating entities to cooperate. Recent models on evaluating trustworthiness in distributed systems have heavily focused on assessing trustworthiness of entities and isolate misbehaviours based on single trust metrics. Less effort has been put on the investigation of the subjective nature and differences in the way trustworthiness is perceived to produce a composite multidimensional trust metrics to overcome the limitation of considering single trust metric. In the light of this context, this thesis concerns the evaluation of entities’ trustworthiness by the design and investigation of trust metrics that are computed using multiple properties of trust and considering environment. Based on the concept of probabilistic theory of trust management technique, this thesis models trust systems and designs cooperation techniques to evaluate trustworthiness in mobile ad hoc networks (MANETs). A recommendation based trust model with multi-parameters filtering algorithm, and multidimensional metric based on social and QoS trust model are proposed to secure MANETs. Effectiveness of each of these models in evaluating trustworthiness and discovering misbehaving nodes prior to interactions, as well as their influence on the network performance has been investigated. The results of investigating both the trustworthiness evaluation and the network performance are promising. / Ministry of Higher Education in Libya and the Libyan Cultural Attaché bureau in London

Music listening is considered one of the most ubiquitous activities in everyday life, and one of the main reasons why people listen is to affect and regulate their mood. The vast availability and unlimited access of music has made it difficult to find relevant music that fits both the context and the preferences of the music listener. The aim of this project was to investigate the personalized relationship between music and mood using everyday technologies, focusing on how a listening experience could be adapted to the desired affect of a music listener while also taking the user’s individual listening history into account. In large, the project concentrated on the possibility of using context-aware music recommendation to generate personalized playlists by focusing on the audio features and corresponding mood of the music. A web-based application was developed to act as a prototype for the study, where the application allowed users to connect to Spotify, pick a desired mood and generate a playlist. By allowing people to access music in this personalized way, a user study could be conducted in order to investigate their music listening while incorporating this recommendation tool. The findings showed that the users’ found the experience to be engaging in that they could use the application as a companion to everyday tasks in addition to it being a tool for getting new, personalized music recommendations. Overall, the participants also found the generated playlists to be accurate to their music preferences and desired affective state.

Nowadays people and organizations are more and more interconnected in the forms of social networks: the nodes are social entities and the links are various relationships among them. The social network theory and the methods of social network analysis (SNA) are being increasingly used to study such real-world networks in order to support knowledge management and decision making in organizations. However, most existing social network studies focus on the static topologies of networks. The dynamic network link formation process is largely ignored. This dissertation is devoted to study such dynamic network formation process to support knowledge management and decision making in networked environments. Three challenges remain to be addressed in modeling and analyzing the dynamic network link formation processes. The first challenge is about modeling the network topological changes using longitudinal network data. The second challenge is concerned with examining factors that influence formation of links among individuals in networks. The third challenge is regarding link prediction in evolving social networks. This dissertation presents four essays that address these challenges in various knowledge management domains. The first essay studies the topological changes of a major international terrorist network over a 14-year period. In addition, this paper used a simulation approach to examine this network's vulnerability to random failures, targeted attacks, and real world authorities' counterattacks. The second essay and third essay focuses on examining determinants that significantly influence the link formation processes in social networks. The second essay found that mutual acquaintance and vehicle affiliations facilitate future co-offending link formation in a real-world criminal network. The third essay found that homophily in programming language preference, and mutual are determinants for forming participation links in an online Open Source social network. The fourth essay focuses on the link prediction in evolving social networks. It proposes a novel infrastructure for describing and utilizing the discovered determinants of link formation process (i.e. semantics of social networks) in link prediction to support expert recommendation application in an Open Source developer community. It is found that the integrated mechanism outperforms either user-based or Top-N most recognized mechanism.

Stocker de grandes quantités de données complexifie les interactions avec les systèmes de Business Intelligence (BI). Les systèmes de recommandation semblent un choix logique pour aider les utilisateurs dans leur analyse. Ils extraient des comportements de données historiques et suggèrent des actions personnalisées, potentiellement redondantes, via des scores de similarité. La diversité est essentielle pour améliorer la satisfaction des utilisateurs, d’où l’intérêt particulier accordé aux recommandations complémentaires. Nous avons étudié deux problèmes concrets d’exploration de données en BI et proposons de découvrir et exploiter les intentions utilisateur pour fournir deux recommandeurs de requête. Le premier, un recommandeur collaboratif réactif original basé sur l’intention, recommande des séquences de requêtes à l’utilisateur pour poursuivre son analyse. Le second propose proactivement un ensemble de requêtes pour compléter un rapport BI, en fonction di contexte utilisateur. / The storage of big amounts of data may lead to a series of long questions towards the expected solution which complicates user interactions with Business Intelligence (BI) systems. Recommender systems appear as a natural solution to help the users complete their analysis. They try to discover user behaviors from the past logs and to suggest personalized actions by predicting lists of likeness scores, which may lead to redundant recommendations. Nowadays, diversity is becoming essential to improve users’ satisfaction, thus, a special interest is dedicated to complementary recommendation. We studied two concrete data exploration problems in BI and we propose to discover and leverage the user intents to provide two query recommenders. The first, an original reactive collaborative Intent-based Recommender, recommends sequences of queries for the user to pursue her analysis. The second one proactively proposes a bundle of queries to complete user BI report, based on the user intents.

The purpose of this study is to investigate the effect of a stock recommendation from the leading financial magazines in Sweden. The study aims to measure the impact a recommendation illustrates in true value. The measurements are mean abnormal returns (AR), mean cumulative abnormal returns (CAR) and mean abnormal volume (AV). Conducting an event study to monitor, not only the date of announcement, but to also validate or invalidate the recommendation as a fundamental changer in the stock case. Where the calculations are made before, on and after the event occurs. With the aim to test if the market is efficient and in line with the rational theories, or if there are other explanatory theories, like the behavioral financial approach, that can explain the results. The sample consists of 571 recommendations that have been announced 2017 and 2018, divided into categories of buy and sell. The sample of buy and sell are also tested in subcategories of small and large companies, to measure the impact due to size of the firm, as a dependent variable. The empirical results shows that there are AR and AV existing due to recommendations. Small companies have the highest measured AR, with sell recommendations having the largest effects. The sell recommendations changes the value and the fundamentals of the stocks, while buy recommendations react positive to the recommendations on the day of announcement, then reverses back to the same price in the end of the event window. Suggesting that the market act both efficient and rational, but also irrational and ineffective, depending on what type of recommendation that is being released and how large and well monitored the company, that gets the recommendation is.

Bakgrund: Riksföreningen för skolsköterskor släppte på 1990-talet en rekommendation om max 400 elever per heltidsarbetande skolsköterska. Sedan dess har en ny Skollag (2010:800) antagits vilken reglerar skolsköterskors uppdrag och innefattar ett basprogram med hälsobesök samt vaccinationer. Uppdraget innefattar även att skolsköterskor ska arbeta hälsofrämjande och förebyggande utifrån elevers livsvärld. Syfte: Belysa skolsköterskors upplevelser av att arbeta hälsofrämjande och förebyggande med ett elevantal som överstiger rekommendationen om max 400 elever per heltidsarbetande skolsköterska. Metod: Studien genomfördes med kvalitativ metod. Tre fokusgruppsdiskussioner med sammanlagt 11 deltagande skolsköterskor genomfördes i södra Sverige. Kvalitativ innehållsanalys användes för att analysera data. Resultat: Analysen av datamaterialet mynnade ut i fyra kategorier: Tidsbrist medför prioritering av arbetsuppgifter, Hög arbetsbelastning på grund av olika faktorer, Hälsofrämjande och förebyggande arbete på gruppnivå samt Rekommendationen i förhållande till skolsköterskors arbetsbelastning och elevers hälsa. Konklusion: Viljan och ambitionen att arbeta hälsofrämjande och förebyggande finns hos skolsköterskor men på grund av tidsbrist prioriteras detta arbete bort. För att ge skolsköterskor möjlighet att arbeta enligt Skollagen (2010:800) behöver elevantalet och även rekommendationen ses över. / Background: In the 1990’s the Swedish Association of School nurses released a recommendation for a maximum of 400 students per full-time school nurse. Since then a new School Law (2010:800) has been adopted which regulates the school nurse's assignments and includes a basic program with health visits and vaccinations. The assignment also includes that school nurses should work on health promotion and prevention based on pupils' life world. Aim: Illuminate school nurses' experiences of working on health promotion and prevention with a student number that exceeds the recommendation of a maximum of 400 students per full-time school nurse. Method: The study was conducted with a qualitative method. Three focus group discussions with a total of 11 participating school nurses were conducted in southern Sweden. Qualitative content analysis was used to analyze data. Results: The analysis of the data material resulted in four categories: Lack of time entails prioritizing tasks, High workload due to various factors, Health promotion and preventive work at group level, and the Recommendation in relation to school nurses 'workload and pupils' health. Conclusion: The desire and ambition to work for health promotion and prevention is found in school nurses, but due to lack of time, this work is not given priority. In order to give school nurses the opportunity to work according to the School Law (2010:800) the number of students and also the recommendation need to be reviewed.