Durch die Vielzahl von heterogenen Informationsquellen sehen sich viele Nutzer einer kaum überschaubaren Informationsflut gegenüber. Aus diesem Grund werden durch diese Arbeit die gängigen Nachrichtenformate analysiert und der aktuelle Stand der Technik im Bereich der Nachrichtenaggregatoren dargelegt. Dabei werden diese Analysen immer mit Blick auf die Möglichkeiten einer personalisierten Filterung der Inhalte durchgeführt. Anschließend wird eine im Rahmen dieser Arbeit entstandene Infrastruktur für die Aggregation, personalisierte Filterung und kollaborative Empfehlung von Inhalten aus heterogenen Nachrichtenquellen vorgestellt. Dabei wird detailiert auf die zu Grunde liegenden Konzepte eingegangen und deren praktische Umsetzung beschrieben.

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.

Fler och fler spel öppnas upp för användargenererat innehåll, vilket ofta resulterar i större mängder material än vad en ensam spelare kan utnyttja. Den unika spelare vill ta del av det som passar just dennes smak.Studien genomfördes med designforskning som metodval och med hjälp av denna metod skapades en artefakt. Med hjälp av den utvecklade artefakten, ett plattformspel som är både enkelt att förstå och spela, kunde en datamängd samlas in ifrån olika spelare. Data byggdes upp av att användarna efter varje slutförd nivå, explicit fick lämna sitt betyg på nivån i en skala mellan 1 och 5.Genom att introducera collaborative filtering och där låta användarens tidigare betyg jämföras med övriga användare kan en predicering av kommande betyg ges. Vid jämförelser av olika collaborative filtering-algoritmer kunde den mest lämpliga upptäckas och senare även användas.Resultaten visar att mer precisa uppskattningar av kommande betyg kan göras av collaborative filteringen än genom att använda nivåns medelbetyg och resultaten leder därför till slutsatsen att collaborative filtering kan ge skräddarsydda spelupplevelser för en unik användare och således förhöja dennes spelupplevelse. / Program: Systemarkitekturutbildningen

With the dramatic growth of the Internet, it is much easier for us to acquire information than before. It is, however, relatively difficult to extract desired information through the huge information pool. One method is to rely on the search engines by analyzing the queried keywords to locate the relevant information. The other one is to recommend users what they may be interested in via recommender systems that analyze the users¡¦ past preferences or other users with similar interests to lessen our information processing loadings. Typical recommendation techniques are classified into content-based filtering technique and collaborative filtering (CF) technique. Several research works in literature have indicated that the performance of collaborative filtering is superior to that of content-based filtering in that it is subject to neither the content format nor users¡¦ past experiences. The collaborative filtering technique, however, has its own limitation of the sparsity problem. To relieve such a problem, researchers proposed several CF-typed variants, including item-based CF and trust-based CF. Few works in literature, however, focus on their performance comparison. The objective of this research is thus to evaluate both approaches under different settings such as the sparsity degrees, data scales, and number of neighbors to make recommendations. We conducted two experiments to examine their performance. The results show that trust-based CF is generally better than item-based CF in sparsity problem. Their difference, however, becomes insignificant with the sparsity decreasing. In addition, the computational time for trust-based CF increases more quickly than that for item-based CF, even though both exhibit exponential growths. Finally, the optimal number of nearest neighbors in both approaches does not heavily depend on the data scale but displays steady robustness.

With the rapid growth of Internet, more and more information is disseminated in the World Wide Web. It is therefore not an easy task to acquire desired information from the Web environment due to the information overload problem. To overcome this difficulty, two major methods, information retrieval and information filtering, arise. Recommender systems that employ information filtering techniques also emerge when the users¡¦ requirements are too vague in mind to express explicitly as keywords. Collaborative filtering (CF) refers to compare novel information with common interests shared by a group of people for recommendation purpose. But CF has major problem: sparsity. This problem refers to the situation that the coverage of ratings appears very sparse. With few data available, the user similarity employed in CF becomes unstable and thus unreliable in the recommendation process. Recently, several collaborative filtering variations arise to tackle the sparsity problem. One of them refers to the item-based CF as opposed to the traditional user-based CF. This approach focuses on the correlations of items based on users¡¦ co-rating. Another popular variation is the trust-based CF. In such an approach, a second component, trust, is taken into account and employed in the recommendation process. The objective of this research is thus to propose a hybrid approach that takes both advantages into account for better performance. We propose the item-level trust-based collaborative filtering (ITBCF) approach to alleviate the sparsity problem. We observe that ITBCF outperforms TBCF in every situation we consider. It therefore confirms our conjecture that the item-level trusts that consider neighbors can stabilize derived trust values, and thus improve the performance.

Repetitions in child-directed speech (CDS) have been shown to vary over time, and are suggested to affect first language acquisition. Correlations between verbal contents of repetitions in CDS and children’s language development have been suggested. The verbal contents of repetitions in Swedish CDS have not yet been investigated. The aim of this study was to examine the verbal contents of repetitions in Swedish CDS during the child’s first 2 years and possible changes in proportions of repetitions during the same time span. Verbal contents of repetitions in parents’ speech in 10 parent-child dyads as the children were 3, 6, 9, 12 and 24 months old were investigated focusing on word classes, sentence types and whole-constituent change. The results were compared to the children’s productive vocabularies at the age of 30 months. Possible occurrences of item-based constructions and frequent frames in the repetitions were also examined. The overall results revealed patterns concerning change in verbal contents in repetitions over time and correlations between verbal contents in repetitions and child language development. Two proposals were made: parents adjust the complexity of their speech to linguistic developmental stages of their children, and linguistic variation in the input increases as the child grows older. / Repetitioner i barnriktat tal (BRT) har visat sig variera över tid, och har föreslagits påverka förstaspåksinlärning. Även ett samband mellan det verbala innehållet i repetitioner i BRT och barns språkutveckling har föreslagits. Det verbala innehållet i repetitioner i svenskt BRT har inte undersökts tidigare. Syftet med denna studie var att undersöka det verbala innehållet i repetitioner i svenskt BRT under barnets två första år och möjliga förändringar gällande andelen repetitioner under samma tidsperiod. Det verbala innehållet i repetitioner i föräldrars tal hos tio förälder-barn-dyader då barnen var 3, 6, 9, 12 och 24 månader gamla undersöktes med fokus på ordklasser, satstyper och förändringar gällande konstituenter. Resultaten jämfördes med barnens produktiva ordförråd vid 30 månaders ålder. Även den möjliga förekomsten av typbaserade konstruktioner (item-based constructions) och frekventa ramar (frequent frames) undersöktes. De övergripande resultaten uppvisade mönster gällande förändringar inom det verbala innehållet i repetitioner över tid samt ett samband mellan det verbala innehållet i repetitioner och barns språkutveckling. Två antaganden gjordes: föräldrar justerar komplexiteten i sitt tal efter språkliga utvecklingsfaser hos sina barn, och den språkliga variationen i inputen ökar med barnets ålder. / MINT: Modelling infant language acquisition from parent-child interaction (MAW 2011.007)

Durch die Vielzahl von heterogenen Informationsquellen sehen sich viele Nutzer einer kaum überschaubaren Informationsflut gegenüber. Aus diesem Grund werden durch diese Arbeit die gängigen Nachrichtenformate analysiert und der aktuelle Stand der Technik im Bereich der Nachrichtenaggregatoren dargelegt. Dabei werden diese Analysen immer mit Blick auf die Möglichkeiten einer personalisierten Filterung der Inhalte durchgeführt. Anschließend wird eine im Rahmen dieser Arbeit entstandene Infrastruktur für die Aggregation, personalisierte Filterung und kollaborative Empfehlung von Inhalten aus heterogenen Nachrichtenquellen vorgestellt. Dabei wird detailiert auf die zu Grunde liegenden Konzepte eingegangen und deren praktische Umsetzung beschrieben.

This paper explores how known machine learning techniques can be applied in unique ways to simplify software and therefore dramatically increase its usability. As software has increased in popularity, its complexity has increased in lockstep, to a point where it has become burdensome. By shifting the focus from the software to the user, great advances can be achieved by way of simplification. The example problem used in this report is well known: suggest local dining choices tailored to a specific person based on known habits and those of similar people. By analyzing past choices and applying likely probabilities, assumptions can be made to reduce user interaction, allowing the user to realize the benefits of the software faster and more frequently. This is accomplished with Java Servlets, Apache Mahout machine learning libraries, and various third party resources to gather dimensions on each recommendation. / text