Making personalized recommendations has become a central part in many platforms, and is continuing to grow with more access to massive amounts of data online. Giving recommendations based on the interests of the individual, rather than recommending items that are popular, increases the user experience and can potentially attract more customers when done right. In order to make personalized recommendations, many platforms resort to machine learning algorithms. In the context of food recipes, these machine learning algorithms tend to consist of hybrid methods between collaborative filtering, content-based methods and matrix factorization. Most content-based approaches are ingredient based and can be very fruitful. However, fetching every single ingredient for recipes and processing them can be computationally expensive. Therefore, this paper investigates if clustering recipes according to what cuisine they belong to and what the main protein is can also improve rating predictions compared to when only collaborative filtering and matrix factorization methods are employed. This suggested content-based approach has a structure of a hierarchical classification, where recipes are first clustered into what cuisine group they belong to, then the specific cuisine and finally what the main protein is. The results suggest that the content-based approach can improve the predictions slightly but not significantly, and can help reduce the sparsity of the rating matrix to some extent. However, it suffers from heavily sparse data with respect to how many rating predictions it can give. / Att ge personliga rekommendationer har blivit en central del av många plattformar och fortsätter att bli det då tillgången till stora mängder data har ökat. Genom att ge personliga rekommendationer baserat på användares intressen, istället för att rekommendera det som är populärt, förbättrar användarupplevelsen och kan attrahera fler kunder. För att kunna producera personliga rekommendationer så vänder sig många plattformar till maskininlärningsalgoritmer. När det kommer till matrecept, så brukar dessa maskininlärningsalgoritmer bestå av hybrida metoder som sammanfogar collaborative filtering, innehållsbaserande metoder och matrisfaktorisering. De flesta innehållsbaserande metoderna baseras på ingredienser och har visats vara effektiva. Däremot, så kan det vara kostsamt för datorer att ta hänsyn till varenda ingrediens i varje matrecept. Därför undersöker denna artikel om att klassificera recept hierarkiskt efter matkultur och huvudprotein också kan förbättra rekommendationer när bara collaborative filtering och matrisfaktorisering används. Denna innehållsbaserande metod har en struktur av hierarkisk klassificering, där recept först indelas efter matkultur, specifik matkultur och till slut vad huvudproteinet är. Resultaten visar att innehållsbaserande metoden kan förbättra receptförslagen, men inte på en statistisk signifikant nivå, och kan reducera gleshet i en matris med tillsatta betyg från olika användare med olika recept något. Däremot så påverkas den ansenligt när det är glest med tillgänglighet av data. / Eatit

The field of personalized product recommendation systems has seen tremendous growth in recent years. The usefulness of the algorithms’ abilities to filter out data from vast sets has been shown to be crucial in today’s information-heavy online experience. Our goal is therefore to compare two recommender models, one based on Random Indexing, the other on Collaborative Filtering, in order to find out if one is better suited to the task than the other. We bring up relevant previous research to set the context for our study, its limitations and possibilities. We then explain the theories, models and algorithms underlying our two recommender systems and finally we evaluate them, partly through empirical data collection from our employer Kavall’s platform, and partly through analysing data from interviews. We judge that our study is scientifically relevant as it compares an algorithm that is rarely used in this context, Random Indexing, to a more established recommendation algorithm, Collaborative Filtering, and as such the result of this comparison might give useful insights into the further development of new or existing algorithms. While more testing is required, the study did show signs that Random Indexing does have the potential of outperforming Collaborative Filtering in some areas, and further development of the model might be a worthwhile endeavor. / Området för personliga produktrekommendationer har sett en enorm tillväxt under de senaste Åren. Användbarheten av algoritmernas förmåga att filtrera ut data ur stora uppsättningar har visat sig vara avgörande i dagens informationstunga onlineupplevelse. Vårt mål Är därför att jämföra två rekommendatormodeller, en baserad på Random Indexing, den andra på Collaborative Filtering, för att ta reda på om den ena Är bättre lämpad för uppgiften Än den andra. Vi tar upp relevant tidigare forskning för att sätta sammanhanget för vår studie, dess begränsningar och möjligheter. Vi förklarar sedan de teorier, modeller och algoritmer som ligger till grund för våra två rekommendationssystem och slutligen utvärderar vi dem, dels genom empirisk datainsamling från vår arbetsgivare Kavalls plattform, dels genom att analysera data från intervjuer. Vi bedömer att vår studie Är vetenskapligt relevant då den jämför en algoritm som sällan används i detta sammanhang, Random Indexing, med en mer etablerad rekommendationsalgoritm, Collaborative Filtering, och som sådan kan resultatet av denna jämförelse ge användbara insikter i den fortsatta utvecklingen av nya eller befintliga algoritmer. även om fler tester krävs, visade studien tecken på att Random Indexing har potentialen att överträffa Collaborative Filtering på vissa områden, och vidareutveckling av modellen kan vara ett givande åtagande.

Recommender systems have become an integral part of virtually every e-commerce application on the web. These systems enable users to quickly discover relevant products, at the same time increasing business value. Over the past decades, recommender systems have been modeled using numerous machine learning techniques. However, the adoptability of these models by commercial applications remains unclear. We assess the receptiveness of the industrial sector to algorithmic contributions of the research community by surveying more than 30 e-commerce platforms, and experimenting with various recommenders on proprietary e-commerce datasets. Another overlooked but important factor that complicates the design and use of recommender systems is their ethical implications. We identify and summarize these issues in our ethical recommendation framework, which also enables users to control sensitive moral aspects of recommendations via the proposed “ethical toolbox”. The feasibility of this tool is supported by the results of our user study. Because of moral implications associated with user profiling, we investigate algorithms capable of generating user-agnostic recommendations. We propose an ensemble learning scheme based on Thompson Sampling bandit policy, which models arms as base recommendation functions. We show how to adapt this algorithm to realistic situations when neither arm availability nor reward stationarity is guaranteed.

在人們的日常生活中，推薦是很普遍的一種社會行為，它使人們不必親自去體驗所有的事物，可透過別人的經驗來得知一件事情或商品的好或壞。隨著科技的快速發展與網際網路的普及，電子商務已逐漸的融入社會，成為人類生活中不可或缺的一部分。然而在網路上過量的資訊，使得個人在資訊的使用與搜尋上面臨極大的挑戰，更加刺激了對於推薦資訊的需求，因此許多推薦技術相繼提出，推薦系統也應運而生，不僅使得推薦的範圍擴大了，推薦的型態也更為豐富多元；同時，在近年電子商務的發展中，對於個人化與顧客導向服務的愈益重視，使得推薦系統逐漸成為一種必要的線上服務。 在眾多的推薦技術之中，協同過濾推薦方法是最成功且最常被採用的推薦技術之一，許多台灣的拍賣平台上也都有採用類似概念的推薦系統，像是Yahoo!拍賣、露天拍賣上的評價機制均屬此類。然而，現行的拍賣評價機制都沒有採用社會網路的技術，本研究希望透過協同過濾與社會網路的結合，讓評價機制更趨於完備。 本研究以台灣最大的拍賣網站Yahoo!為例，蒐集了44萬筆交易記錄，並以推薦網(ReferralWeb)系統的矩陣方法為基礎，找出人與商品的關係、商品與類別的關係、人與人的關係，建立起一個社會網路，讓使用者可查詢特定領域的專家，並與之交易。除此之外，也可直接詢問專家關於商品的資訊或購買技巧。透過這樣的機制，希望能降低消費者在購買商品時所產生的交易糾紛，讓人們在網路上的購物體驗能變得更好。 / Nowadays, recommendation is a common social behavior between people. People can evaluate things or commodities from others’ experience and opinions instead of their own experiences. Along with the development of technology and Internet today, E-commerce has become an indispensable part of human life. However, due to the overloaded information, people face a fantastic challenge when accessing and searching on the Internet. Therefore, many methods of recommendation were proposed, and systems of recommendation are to come with the tide of fashion. In addition, the development of E-commerce emphasized on personalization and customer-oriented services more in recent years, which make recommendation system becomes a necessary on-line service gradually. Collaborative Filtering is the most successful and adopted one in numerous recommendation methods. There are many auction platforms in Taiwan also use recommendation systems, such like "Yahoo Auction", "Ruten Auction", etc. However, the previous mentioned recommendation mechanisms haven’t used Social Network technology; this study will propose an recommendation system which combines Collaborative Filtering and Social Network technology. This research collects 440,000 transaction data from the Yahoo auction platform, which is the biggest auction website in Taiwan. Based on the matrix method of ReferralWeb system(Shah, 1997), this research would like to build up the matrix of relationships between Person-Commodity, Commodity-Category, and Person-Person. Based on the three matrixes, finally builds up a Social Network. In the Social Network, users can enquire experts refer to the specific category of commodity, and then refer to the shops which the experts like or directly ask them the commodity information and purchase skill. Relying on the mechanism proposed by this research, our goals are to reduce the transaction disputes arising from consumers purchase commodities, and to let people have better experiences in on-line shopping.

This work deals with the issue of poviding recommendations for individual users of e-shop based on the obtained user preferences. The work includes an overview of existing recommender systems, their methods of getting user preferences, the methods of using objects' content and recommender algorithms. An integral part of this work is design and implementated for independent software component for Content-based recommendation. Component is able to receive various user preferences and various forms of object's input data. The component also contains various processing methods for implicit feedback and various methods for making recommendations. Component is written in the Java programming language and uses a PostgreSQL database. The thesis also includes experiments that was carried out with usage of component designed on datasets slantour.cz and antikvariat-ichtys.cz e-shops.

Recommender systems are programs that aim to present items like songs or books that are likely to be interesting for a user. These systems have become increasingly popular and are intensively studied by research groups all over the world. In web systems, like e-shops or community servers there are usually multiple data sources we can use for recommending, as user and item attributes, user-item rating or implicit feedback from user behaviour. In the thesis, we present a concept of a Universal Recommender System (Unresyst) that can use these data sources and is domain-independent at the same time. We propose how Unresyst can be used. From the contemporary methods of recommending, we choose a knowledge based algorithm combined with collaborative filtering as the most appropriate algorithm for Unresyst. We analyze data sources in various systems and generalize them to be domain-independent. We design the architecture of Unresyst, describe its interfaces and methods for processing the data sources. We adapt Unresyst to three real-world data sets, evaluate the recommendation accuracy results and compare them to a contemporary collaborative filtering recommender. The comparison shows that combining multiple data sources can improve the accuracy of collaborative filtering algorithms and can be used in systems where...

Com o propósito de auxiliar os usuários no processo de tomada de decisão, diversos tipos de sistemas Web passaram a incorporar sistemas de recomendação. As abordagens mais utilizadas são a filtragem baseada em conteúdo, que recomenda itens com base nos seus atributos, a filtragem colaborativa, que recomenda itens de acordo com o comportamento de usuários similares, e os sistemas híbridos, que combinam duas ou mais técnicas. A abordagem baseada em conteúdo apresenta o problema de análise limitada de conteúdo, o qual pode ser reduzido com a utilização de informações semânticas. A filtragem colaborativa, por sua vez, apresenta o problema da partida fria, esparsidade e alta dimensionalidade dos dados. Dentre as técnicas de filtragem colaborativa, as baseadas em fatoração de matrizes são geralmente mais eficazes porque permitem descobrir as características subjacentes às interações entre usuários e itens. Embora sistemas de recomendação usufruam de diversas técnicas de recomendação, a maioria das técnicas apresenta falta de informações semânticas para representarem os itens do acervo. Estudos na área de sistemas de recomendação têm analisado a utilização de dados abertos conectados provenientes da Web dos Dados como fonte de informações semânticas. Dessa maneira, este trabalho tem como objetivo investigar como relações semânticas computadas a partir das bases de conhecimentos disponíveis na Web dos Dados podem beneficiar sistemas de recomendação. Este trabalho explora duas questões neste contexto: como a similaridade de itens pode ser calculada com base em informações semânticas e; como semelhanças entre os itens podem ser combinadas em uma técnica de fatoração de matrizes, de modo que o problema da partida fria de itens possa ser efetivamente amenizado. Como resultado, originou-se uma métrica de similaridade semântica que aproveita a hierarquia das bases de conhecimento e obteve um desempenho superior às outras métricas na maioria das bases de dados. E também o algoritmo Item-MSMF que utiliza informações semânticas para amenizar o problema de partida fria e obteve desempenho superior em todas as bases de dados avaliadas no cenário de partida fria. / In order to assist users in the decision-making process, several types of web systems started to incorporate recommender systems. The most commonly used approaches are content-based filtering, which recommends items based on their attributes; collaborative filtering, which recommends items according to the behavior of similar users; and hybrid systems that combine both techniques. The content-based approach presents the problem of limited content analysis, which can be reduced by using semantic information. The collaborative filtering, presents the problem of cold start, sparsity and high dimensionality of the data. Among the techniques of collaborative filtering, those based on matrix factorization are generally more effective because they allow us to discover the underlying characteristics of interactions between users and items. Although recommender systems have several techniques, most of them lack semantic information to represent the items in the collection. Studies in this area have analyzed linked open data from the Web of data as source of semantic information. In this way, this work aims to investigate how semantic relationships computed from the knowledge bases available in the Data Web can benefit recommendation systems. This work explores two questions in this context: how the similarity of items can be calculated based on semantic information and; as similarities between items can be combined in a matrix factorization technique, so that the cold start problem of items can be effectively softened. As a result, a semantic similarity metric was developed that leverages the knowledge base hierarchy and outperformed other metrics in most databases. Also the Item-MSMF algorithm that uses semantic information to soften the cold start problem and obtained superior performance in all databases evaluated in the cold start scenario.

A crescente oferta de programas de código aberto na rede mundial de computadores expõe potenciais usuários a muitas possibilidades de escolha. Em face da pluralidade de interesses desses indivíduos, mecanismos eficientes que os aproximem daquilo que buscam trazem benefícios para eles próprios, assim como para os desenvolvedores dos programas. Este trabalho apresenta o AppRecommender, um recomendador de aplicativos GNU/Linux que realiza uma filtragem no conjunto de programas disponíveis e oferece sugestões individualizadas para os usuários. Tal feito é alcançado por meio da análise de perfis e descoberta de padrões de comportamento na população estudada, de sorte que apenas os aplicativos considerados mais suscetíveis a aceitação sejam oferecidos aos usuários. / The increasing availability of open source software on the World Wide Web exposes potential users to a wide range of choices. Given the individuals plurality of interests, mechanisms that get them close to what they are looking for would benefit users and software developers. This work presents AppRecommender, a recommender system for GNU/Linux applications which performs a filtering on the set of available software and individually offers suggestions to users. This is achieved by analyzing profiles and discovering patterns of behavior of the studied population, in a way that only those applications considered most prone to acceptance are presented to users.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-08-29T14:58:03Z No. of bitstreams: 1 Eduardo Vieira.pdf: 6054087 bytes, checksum: 24f8532bfdfaeef177aa46b9f5974869 (MD5) / Made available in DSpace on 2017-08-29T14:58:03Z (GMT). No. of bitstreams: 1 Eduardo Vieira.pdf: 6054087 bytes, checksum: 24f8532bfdfaeef177aa46b9f5974869 (MD5) Previous issue date: 2007-02-12 / This work introduces PROPOST (Project Portfolio Support Tool), a knowledgebased software tool for supporting Project Portfolio Management – an increasing management model nowadays. This tool focuses on a project definition process, and was modeled using the MAAEM methodology and the ONTORMAS ontology-driven tool, as well as by reusing the ONTOINFO and ONTOWUM ontologies, which describe software product families for the development of Information Retrieval and Filtering applications, respectively. PROPOST looks for providing resource optimization by supporting reuse of existing information systems as well as avoiding duplicity on project definition for the composition on the organization’s software portfolio. The tool was created not only as a contribution for solving a current problem related to redundancy on portfolio definition, as well as support for several activities related to portfolio management (select, prioritization and evaluation). The development of PROPOST provides references on how ontology-based development can help in the software development process. It also contributes as a case study for evaluating the MAAEM methodology and the ONTORMAS ontology used in modeling process, having provided several hints for their improvement. / Este trabalho apresenta a PROPOST (Project Portfolio Support Tool), uma ferramenta baseada em conhecimento, para suporte à Gestão de Portifólio de Projetos – um modelo de gestão em ascensão na atualidade. Esta ferramenta possui seu foco no processo de definição de projetos, e foi modelada usando a metodologia MAAEM e a ferramenta dirigida por ontologias ONTORMAS, bem como pelo reuso das ontologias ONTOINFO e ONWOWUM, as quais descrevem famílias de produtos de software para o desenvolvimento de aplicações nas áreas de Recuperação e Filtragem de Informação, respectivamente. A PROPOST objetiva promover a otimização de recursos através da reutilização de sistemas de informação existentes, bem como evitar duplicidade na definição de projetos para a composição do portifólio de software das empresas. Sendo assim, a concepção desta ferramenta objetivou contribuir para a solução de um problema da atualidade, relacionado à redundância na composição do portifólio de projetos, bem como suporte a outras atividades relacionadas à gestão do portifólio (seleção, priorização e avaliação). O desenvolvimento da PROPOST também serve de referência sobre as contribuições das ontologias no processo de desenvolvimento de software. Adicionalmente, esse trabalho também constituiu um estudo de caso para avaliação da metodologia MAAEM e da ontologia ONTORMAS usadas no processo de modelagem, tendo proporcionado várias contribuições para a melhoria das mesmas.

Recommendation systems are gaining more popularity because of the complexity of problems that they provide a solution to. There are many applications of recommendation systems everywhere around us. Implementation of these systems differs and there are two approaches that are most distinguished. First approach is a system without Machine Learning, while the other one includes Machine Learning. The second approach, used in this project, is based on Machine Learning collaborative filtering techniques. These techniques include numerous algorithms and data processing methods. This document describes a process that focuses on building a job recommendation system for a recruitment industry, starting from data acquisition to the final result. Data used in the project is collected from the Pitchler AB company, which provides an online recruitment platform. Result of this project is a machine learning based recommendation system used as an engine for the Pitchler AB IT recruitment platform.