РЕКОМЕНДАТЕЛЬНАЯ СИСТЕМА ДЛЯ ОНЛАЙН-МАГАЗИНОВ С ИСПОЛЬЗОВАНИЕМ МАШИННОГО ОБУЧЕНИЯ

Recommender systems can be singled out among the latest trends in Internet marketing. Recommender systems are special applications focused on predicting the interests and needs of potential customers of online stores, which are convenient tools for choosing when buying goods and services in online stores. It is fundamentally important that recommendation services areuseful and convenient for both the user and the online store. The user, first of all, has the convenience and intuitiveness of the choice. At the same time, the store opens up such opportunities as increasing the average check and revenue per visit, alternative navigation in the entire variety of products and a source of customer information. Today, modern recommendation services increase the content of online shopping carts by 12-60%, which usually depends on the profile of the product.Интернет-маркетингтің соңғы тенденцияларының арасында ҧсыныс жҥйелерін бҿлуге болады. Ҧсынушы жҥйелер – бҧл интернет-дҥкендердің тауарлары мен қызметтерін сатып алу кезінде таңдаудың ыңғайлы қҧралы болып табылатын интернетдҥкендердің ҽлеуетті клиенттерінің қызығушылықтары мен қажеттіліктерін болжауға бағытталған арнайы қосымшалар. Ҧсыныс қызметтері пайдаланушы ҥшін де, интернет-дҥкен ҥшін де пайдалы жҽне ыңғайлы болуы ҿте маңызды. Ең алдымен қолданушының ыңғайлы жҽне интуитивті таңдауы бар. Сонымен қатар дҥкенге бару кезінде орташа чек пен кірісті ҧлғайту, тауарлардың алуан тҥріндегі альтернативті навигация жҽне тҧтынушылар туралы дереккҿзі ақпарат мҥмкіндіктерін ашады. Бҧл, ҽдетте ҿнімнің профиліне байланысты, бҥгінгі таңда заманауи ҧсынымдық қызметтер онлайн-дҥкен арбаларының қҧрамын 12-60%-ға арттырады.Среди множества последних трендов интернет-маркетинга можно отметить рекомендательные системы. Рекомендательные системы — особые приложения, направленные на прогнозирование интересов и потребностей вероятных покупателей интернет-магазинов, являющиеся комфортным инструментом выбора при приобретениипродуктов и предложений в онлайн-магазинах. Принципально важными факторами, влияющими на развитие рекомендательных сервисов, являются польза и удобство одновременно и для потребителя, и для интернет-магазина. Пользователь, прежде всего, получает удобство интуитивного выбора. Для магазина открываются такие возможности, как увеличение среднего чека и выручки компании, альтернативная навигация во всем множестве товаров и получение информации о клиентах. Современные рекомендательные сервисы повышают наполненность онлайн-корзин на 12–60%, что обычно зависит от профильной направленности продукции

Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information

Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, contributing to phenomena such as filter bubbles and polarization. In this work, we consider directly the information availability problem through the lens of user recourse. Using ideas of reachability, we propose a computationally efficient audit for top-$N$ linear recommender models. Furthermore, we describe the relationship between model complexity and the effort necessary for users to exert control over their recommendations. We use this insight to provide a novel perspective on the user cold-start problem. Finally, we demonstrate these concepts with an empirical investigation of a state-of-the-art model trained on a widely used movie ratings dataset.Comment: appeared at FAccT '2

Sequencing in Intelligent Tutoring Systems based on online learning Recommenders

In dieser Arbeit entwickeln und testen wir Algorithmen für Learning Analytics, die die personalisierte Sequenzierung von Matheaufgaben erlauben. Die Sequenzierung schlägt die nächste Aufgabe einem Schüler vor, die seine Lernbedürfnisse entspricht. Unsere Lösung basiert auf Vygotskys „Zone of Proximal Development“ (ZPD), das die weder zu einfachen noch zu schwierigen Aufgaben für den Schüler bestimmt. Der Sequenzer, auch Vygotsky Policy Sequencer genannt, ist in der Lage Aufgaben im ZPD zu erkennen, dank die von einem Vorhersagealgorithmus geschätzte zukünftige Leistung des Schülers. Die Arbeit enthält folgende Beiträge: (1) Die Evaluation der Anwendbarkeit von Matrix Factorization als Inhaltsdomäne unabhängige Algorithmus für die Vorhersage der Leistung der Schüler. (2) Anpassung und Evaluation eines Matrix Factorization basierenden Algorithmus, der die zeitliche Evolution der Schülerkenntnisse einbezieht. (3) Entwicklung von zwei Ansätzen für die Aktualisierung von Matrix Factorization basierenden Modellen durch den Kalman Filter. Zwei Aktualisierungsfunktionen sind benutzt: (a) eine einfache, die nur die letzte vom Schüler gesehene Aufgabe betrachtet, und (b) eine, die in der Lage ist, seine fehlenden Kompetenzen einzuschätzen. (4) Ein neues Verfahren von Machine Learning gesteuerte Sequenzer zu testen durch die Modellierung einer simulierten Umgebung, die aus simulierte Schülern und Aufgaben mit stetigen erzielten und gebrauchten Fähigkeiten und Schwierigkeitsgraden besteht. (5) Die Entwicklung einer minimal eingreifenden API für die leichte Integration von Machine Learning basierende Komponente in größere Systeme, um das Integrationsrisiko und die Kosten vom Know-How-Transfer zu minimieren. Dank all diesen Beiträgen, wurde der VPS in ein großes kommerzielles System integriert und mit 100 Kinder für einen Monat getestet. Der VPS zeigte Lerneffekte und wahrgenommene Erlebnisse, die mit den von den ITS Sequenzer vergleichbar sind. Infolge der besseren VPS Modellierfähigkeiten konnten die Schüler beendeten die Aufgaben schneller lösen.In this thesis we design and test Learning Analytics algorithms for personalized tasks' sequencing that suggests the next task to a student according to his/her specific needs. Our solution is based on a sequencing policy derived from the Vygotsky's Zone of Proximal Development (ZPD), which denes those tasks that are neither too easy not too dicult for the student. The sequencer, called Vygotsky Policy Sequencer (VPS), can identify tasks in the ZPD thanks to the information it receives from performance prediction algorithms able to estimate the knowledge of the student. Under this context we describe hereafter the thesis contributions. (1) A feasibility evaluation of domain independent Matrix Factorization applied in ITS for Performance Prediction. (2) An adaption and the related evaluation of a domain independent update for online learning Matrix Factorization in ITS. (3) A novel Matrix Factorization update method based on Kalman Filters approach. Two different updating functions are used: (a) a simple one considering the task just seen, and (b) one able to derive the skills' deficiency of the student. (4) A new method for offline testing of machine learning controlled sequencers by modeling simulated environment composed by a simulated students and tasks with continuous knowledge and score representation and different diffculty levels. (5) The design of a minimal invasive API for the lightweight integration of machine learning components in larger systems to minimize the risk of integration and the cost of expertise transfer. Profiting from all these contributions, the VPS was integrated in a commercial system and evaluated with 100 children over a month. The VPS showed comparable learning gains and perceived experience results with those of the ITS sequencer. Finally, thanks to its better modeling abilities, the students finish faster the assigned tasks

Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC