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One of the most important challenges facing us today is to personalize services based on user preferences. In order to achieve this objective, the design of Recommender Systems (RSs), which are systems designed to aid the users through different decision-making processes by providing recommendations to them, have been an active area of research. RSs may produce personalized and non-personalized recommendations. Non-personalized RSs provide general suggestions to a user, based on the number of times an item has been selected in the past. Personalized RSs, on the other hand, aim to predict the most suitable items for a specific user, based on the user’s preferences and constraints. The latter are the focus of this thesis. While Recommender Systems have been successful in many domains, a number of challenges remain. For example, most implementations consider only single criteria ratings, and consequently are unable to identify why a user prefers an item over others. Many systems classify the user into one single group or cluster which is an unrealistic approach, since in real world users share commonalities in different degrees with diverse types of users. Others require a large amount of previously gathered data about users’ interactions and preferences, in order to be successfully applied. In this study, we introduce a methodology for the creation of Personalized Multi Criteria Context Aware Recommender Systems that aims to overcome these shortcomings. Our methodology incorporates the user’s current context information, and techniques from the Multiple Criteria Decision Analysis (MCDA) field of study to analyze and model the user preferences. To this end, we create a multi criteria user preference model to assess the utility of each item for a specific user, to then recommend the items with the highest utility. The criteria considered when creating the user preference model are the user’s location, mobility level and user profile. The latter is obtained by considering the user specific needs, and generalizing the user data from a large scale demographic database. We present a case study where we applied our methodology into PeRS, a personal Recommender System to recommend events that will take place within the Ottawa/Gatineau Region. Furthermore, we conduct an offline experiment performed to evaluate our methodology, as implemented in our case study. From the experimental results we conclude that our RS is capable to accurately narrow down, and identify, the groups from a demographic database where a user may belong, and subsequently generate highly accurate recommendation lists of items that match with his/her preferences. This means that the system has the ability to understand and typify the user. Moreover, the results show that the obtained system accuracy doesn’t depend on the user profile. Therefore, the system is potentially capable to produce equally accurate recommendations for a wide range of the population.

Knowledge discovery is an important process in data analysis, data mining and machine learning. Typically knowledge is presented in the form of rules. However, knowledge discovery systems often generate a huge amount of rules. One of the challenges we face is how to automatically discover interesting and meaningful knowledge from such discovered rules. It is infeasible for human beings to select important and interesting rules manually. How to provide a measure to evaluate the qualities of rules in order to facilitate the understanding of data mining results becomes our focus. In this thesis, we present a series of rule evaluation techniques for the purpose of facilitating the knowledge understanding process. These evaluation techniques help not only to reduce the number of rules, but also to extract higher quality rules. Empirical studies on both artificial data sets and real world data sets demonstrate how such techniques can contribute to practical systems such as ones for medical diagnosis and web personalization. In the first part of this thesis, we discuss several rule evaluation techniques that are proposed towards rule postprocessing. We show how properly defined rule templates can be used as a rule evaluation approach. We propose two rough set based measures, a Rule Importance Measure, and a Rules-As-Attributes Measure, %a measure of considering rules as attributes, to rank the important and interesting rules. In the second part of this thesis, we show how data preprocessing can help with rule evaluation. Because well preprocessed data is essential for important rule generation, we propose a new approach for processing missing attribute values for enhancing the generated rules. In the third part of this thesis, a rough set based rule evaluation system is demonstrated to show the effectiveness of the measures proposed in this thesis. Furthermore, a new user-centric web personalization system is used as a case study to demonstrate how the proposed evaluation measures can be used in an actual application.

In this thesis, we plan to use a group-based semantic-expansion approach to design a new personalised system framework. Semantic web and group preference offer solution to the above problem. In this thesis, ontologies and semantic techniques are applied in different components of the framework. Information has been gathered from different resources and each of the resource might be using various types of identifiers for the same concept, therefore semantic web technologies are used to find out if the concept is the same or not. On the other hand, we create group preference in our personalization system. If the system fails to obtain personal preference from new user, group preference supports the system providing recommendation to the new user according to group classification.

Knowledge discovery is an important process in data analysis, data mining and machine learning. Typically knowledge is presented in the form of rules. However, knowledge discovery systems often generate a huge amount of rules. One of the challenges we face is how to automatically discover interesting and meaningful knowledge from such discovered rules. It is infeasible for human beings to select important and interesting rules manually. How to provide a measure to evaluate the qualities of rules in order to facilitate the understanding of data mining results becomes our focus. In this thesis, we present a series of rule evaluation techniques for the purpose of facilitating the knowledge understanding process. These evaluation techniques help not only to reduce the number of rules, but also to extract higher quality rules. Empirical studies on both artificial data sets and real world data sets demonstrate how such techniques can contribute to practical systems such as ones for medical diagnosis and web personalization. In the first part of this thesis, we discuss several rule evaluation techniques that are proposed towards rule postprocessing. We show how properly defined rule templates can be used as a rule evaluation approach. We propose two rough set based measures, a Rule Importance Measure, and a Rules-As-Attributes Measure, %a measure of considering rules as attributes, to rank the important and interesting rules. In the second part of this thesis, we show how data preprocessing can help with rule evaluation. Because well preprocessed data is essential for important rule generation, we propose a new approach for processing missing attribute values for enhancing the generated rules. In the third part of this thesis, a rough set based rule evaluation system is demonstrated to show the effectiveness of the measures proposed in this thesis. Furthermore, a new user-centric web personalization system is used as a case study to demonstrate how the proposed evaluation measures can be used in an actual application.

Background: We have identified the combination of Knowledge Management and Human Resource Management as interesting because of this constellation has been mentioned scarcely in previous studies. There also seem to be some interesting correlations with personnel turnover. Aim: The aim of this study is to describe and understand the theoretically best combination of Human Resource Management and Knowledge Management, by creating a model. The model is also going to be tested empirically through consulting firms, to see if they meet the ideal combination. With this model we also want to describe in what way the different combinations of strategies will affect the personnel turnover. Definitions: A huge part of this study concerns the theoretical area Human Resource Management, which we have entitled HRM. Similarly, Knowledge Management has been entitled KM. Completion: The study is designed both as a literature review and as a comparative case study in which empirical data has been collected through qualitative interviews with four Swedish management consulting firms. Results: The best combinations of KM and HRM are according to this study that strategies should consist of a thoroughgoing personalization or codification. The result also gives a description of how various HRM-aspects can be adapted in line with the best combination. The result stresses that the companies concerned in these case studies don’t follow these recommended combinations. The result also includes descriptions of the varying effects on personnel turnover that comes along with different combination of KM and HRM. / Bakgrund: Vi har identifierat kombinationen av Knowledge Management och Human Resource Management som intressant då denna konstellation behandlats sparsamt i tidigare studier. Det finns även intressanta samband i termer av personalomsättning som fångat vårt intresse. Syfte: Syftet med denna studie är att skapa en modell för att beskriva och förstå hur de teoretiskt bästa kombinationerna av Human Resource Management och Knowledge Management kan se ut. Modellen ska även testas empiriskt på konsultföretag för att se om de uppfyller idealkombinationerna. I modellen vill vi även beskriva på vilka sätt olika strategikombinationer kan påverka personalomsättningen. Definitioner: Stora delar av studien kretsar kring det teoretiska området Human Resource Management vilket vi har förkortat HRM. På samma sätt har Knowledge Management förkortats som KM. Genomförande: Studien är utformad dels som en litteraturstudie och dels som en komparativ fallstudie där empirin bygger på kvalitativa intervjuer med fyra svenska managementkonsultbolag. Resultat: De bästa kombinationerna av KM och HRM är enligt studien när de genomgående utgörs av personalisering eller kodifiering. Resultatet ger även en beskrivning av hur olika HRM-aspekter kan anpassas i linje med dessa bästa kombinationer. Studien visar även att företag (fallföretagen i denna studie) inte följer idealkombinationerna i praktiken samt att olika kombinationer kan få varierande effekter på företags personalomsättning.

This BA thesis examines how the Swedish newspaper Aftonbladet writes about celebrities.Our questions were: How much does Aftonbladet write about celebrities? What kind ofcelebrities does Aftonbladet write about? In which context do celebrities appear inAftonbladet? We have also studied how these matters have changed since 1978.In our research we have used quantitative content analysis. We have analyzed a total of 956articles about celebrities from 1978, 1988, 1998 and 2008. We chose to analyze articles fromtwo synthetic weeks each year.We have used theories about celebrity culture, popularization and personalization and alsoabout public and private in our analyze.Our conclusions were that Aftonbladet has written a lot of articles about celebrities for a longtime, but the articles about celebrities in Aftonbladet have increased by 170 percent since1978.The number of articles that Aftonbladet has written dealing with the private life of celebritieshas not changed much at all since 1978. This was something that surprised us because weexpected that Aftonbladet would write more about the private life of celebrities in 2008 thanin 1978.

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Literature digital libraries are the source of digitalized literature data, from which Researchers can search for articles that meet their personal interest. However, Users often confused by the large number of articles stored in a digital library and a single query will typically yield a large number of articles, among which only a small subset will indeed interest the user. To provide more effective and efficient information search, many systems are equipped with a recommendation subsystem that recommends articles that users might be interested. In this thesis, we aim to research a number of recommendation techniques for making personalized recommendation. In light of the previous work that used collaborative approach for making recommendation for literature digital libraries, in this thesis, we first propose three content-based recommendation approaches, followed by a set of hybrid approaches that combine both content-based and collaborative methods. These alternatives and approaches were evaluated using the web log of an operational electronic thesis system at NSYSU. It has been found the hybrid approaches yields better quality of articles recommendation.

Owing to the booming development of the WWW, it creates many new challenges for information filtering. Information Filtering (IF) is an area of research that develops tools for discriminating between relevant and irrelevant information. IF can find good matches between the web pages and the users' information needs. Users first give descriptions about what they need, i.e., user profiles, to start the services. A profile index is built on these profiles. A series of incoming web pages will be put into the matching process. Each incoming web page is represented in the same form of the user profile. In this way, the users who are interested in an incoming web page can be identified by comparing the descriptions of the web page with each user profile. At last, the web page will be recommended to the users whose profiles belong to the filtered results. Therefore, a critical issue of the information filtering service is how to index the user profiles for an efficient matching process. When we index the user profile, we can reduce the costs of storage space and the processing time for modifying the user profiles. In this thesis, first, we propose a count-based tree method, which takes the count of each keyword into consideration, to reduce the large storage spaces as needed by the tree method. Next, three large-itemset-based methods are proposed to reduce the storage space, which are called the count-major large itemset method, the weighted large itemset method and the hybrid method. In these three large-itemset-based methods, we first cluster profiles with similar interests into the same group. Next, for each cluster, we apply the mining association rules techniques to help us to construct the index strategies. We design three methods by using the idea of the Apriori algorithm which is one of well-known approaches in mining association rules. But, we modify the minimum support and the goal in the Apriori algorithm. We may not always output the large itemset Lk. That is, we may only use Lw, where w < k. In summary, the cost of storage spaces of our four methods are less than that of the tree method proposed by Yan and Garcia-Molina. According to our simulation results, each of our four methods may provide the best result when different input data sets. Next, we propose a large-itemset-based approach to the incremental update of the index structure for storing keywords to reduce the update cost. When someone's interests are often changed, we must care about the way how to provide the low update cost of the index structure. We take the weight of each keyword into consideration. That is, each keyword can be distinguished the long-term interest which has weight above the threshold from the short-term interest which has weight below the threshold. Owing to that the probability of modifying the short-term interests is higher than that of modifying the long-term interests, we can update the short-term interests locally. According to our simulation results, our method really can reduce the update cost as needed by Wu and Chen' methods.