Searching the Web involves more than sifting through a huge graph of pages and hyperlinks. Specific collaboration networks have emerged that serve domain-specific queries better by exploiting the principles and patterns that apply there. We continue this trend by suggesting heuristics and algorithms to mine the evolution of collaboration networks, to discover interesting roles played by entities. The first section of the dissertation introduces the concept of nurturers using the computer science research community as a case study, while the second section formulates three roles - scouts, promoters and connectors, played by ratings in collaborative filtering systems. Nurturers: Nurturing, a pervasive mammalian trait, naturally extends to most association networks that involve humans. The increased availability of digital and online data about associations lets researchers experiment with algorithms to gain insight into such phenomena. Consider some examples of nurturing: • Slashdot endorsement. Slashdot was not the first site to link to Firefox, but the publicity Firefox received from this association surely helped it become popular quickly. The phenomenon of many small websites crashing due to publicity received through Slashdot has become well known as the Slashdot Effect. • A VC (Venture Capitalist) seed-funding a new startup. This event has a high nurturing value if the startup’s valuation increases rapidly after the funding. • A blogger writing about a topic. Kim Cameron has nurtured the “Laws of Identity” topic if it later becomes the buzz in blog circles. A nurturer need not always be the innovator or originator. The evangelist who adopts a prodigal idea and launches it on its way to success can also be a nurturer. • A professor guiding his student through the art of scientific research and bootstrapping him into a vibrant research community. New nodes not only emerge around these nurturers, but also become important in the network. Knowing nurturers is useful especially in vertical search, where algorithms exploit the structure of specialized collaboration networks to make search more relevant: knowing early adopters of good web pages can make web-search fresher; a list of VCs ranked by their nurturing value is useful to people with new startup ideas; the list of top nurturers in computer science is a valuable resource for a student seeking to do research. This dissertation presents a framework for discovering nurturers by mining the evolution of an association network, and discusses heuristics and customizations that can be applied through a case study: finding the Best Nurturers in Computer Science Research. Roles of Ratings in Collaborative Filtering: Recommender systems aggregate individual user ratings into predictions of products or services that might interest visitors. The quality of this aggregation process crucially affects user experience and hence the effectiveness of recommenders in e-commerce. The dissertation presents a novel study that disaggregates global recommender performance metrics into contributions made by each individual rating, allowing us to characterize the many roles played by ratings in nearest neighbor collaborative filtering. In particular, we formulate three roles - scouts, promoters, and connectors that capture how users receive recommendations, how items get recommended, and how ratings of these two types are themselves connected (respectively). These roles find direct uses in improving recommendations for users, in better targeting of items, and most impor -tantly, in helping monitor the health of the system as a whole. For instance, they can be used to track the evolution of neighborhoods, to identify rating subspaces that do not contribute (or contribute negatively) to system performance, to enumerate users who are in danger of leaving, and to assess the susceptibility of the System to attacks such as shilling. The three rating roles presented here provide broad primitives to manage a recommender system and its community.

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

Recommendation systems are emerging as an important business application as the demand for personalized services in E-commerce increases. Collaborative filtering techniques are widely used for predicting a user's preference or generating a list of items to be recommended. In this thesis, we develop several new approaches for collaborative filtering based on model combination and kernel smoothing. Specifically, we start with an experimental study that compares a wide variety of CF methods under different conditions. Based on this study, we formulate a combination model similar to boosting but where the combination coefficients are functions rather than constant. In another contribution we formulate and analyze a local variation of matrix factorization. This formulation constructs multiple local matrix factorization models and then combines them into a global model. This formulation is based on the local low-rank assumption, a slightly different but more plausible assumption about the rating matrix. We apply this assumption to both rating prediction and ranking problems, with both empirical validations and theoretical analysis. We contribute with this thesis in four aspects. First, the local approaches we present significantly improve the accuracy of recommendations both in rating prediction and ranking problems. Second, with the more realistic local low-rank assumption, we fundamentally change the underlying assumption for matrix factorization-based recommendation systems. Third, we present highly efficient and scalable algorithms which take advantage of parallelism, suited for recent large scale datasets. Lastly, we provide an open source software implementing the local approaches in this thesis as well as many other recent recommendation algorithms, which can be used both in research and production.

This thesis proposes a novel recommendation approach to take advantage of the information available in user-created lists. Our approach assumes associations among any two items appearing in a list together. We consider two different ways to calculate the strength of item-item associations: frequency of co-occurrence, and sum of Bayesian ratings (SBR) of all lists containing the item pair. The latter takes into consideration not only the number of lists the items have co-appeared in, but also the quality of the lists. We collected a data set of user ratings for books along with Listmania lists on Amazon.com using Amazon Web Services (AWS). Our method shows superior performance to existing user-based and item-based collaborative filtering approaches according to the resulted Mean Absolute Error (MAE), coverage, precision and recall.

Studier kring rekommendationsmotorer är ett område med större signifikans i en växande digital verklighet. Mängden med information ökar och med mer information blir det svårare att hitta det som för individen är av intresse. Vissa specifika områden med tillämpning av rekommendationsmotorer är mer välstuderade än andra, domäner som sysslar med försäljning hamnar i den mer studerade kategorin. Andra domäner som är i behov av rekommendationsmotorer, som inte är lika välstuderade är verksamheter som tillhandahåller möjlighet för lärande via internet. En av dessa verksamheter heter Nomp och erbjuder ett läroverktyg för barn och ungdomar inom matematik. Målet med denna studie är därför att implementera en rekommendationsmotor inom denna mindre utforskade domän. Målet är även att undersöka nyttan med rekommendationsmotorn för applikationens användare. Studien har baserats på ett ramverk inom designforskning, vilket inkluderar olika typer av experiment samt en undersökning. Resultaten från dessa aktiviteter utgjorde empirin för den analys som sedan genomfördes. Resultatet ger visst stöd för att det är möjligt att implementera en rekommendationsmotor för denna domän. De visade däremot inget entydigt svar i vilken omfattning dess nytta har för slutanvändaren. Studiens målsättning uppfylldes till viss del, däremot kunde nyttan för slutanvändaren utforskats i större omfattning. Förhoppningen är att denna studie ska ha effekter i form av praktiska konsekvenser, där användare kan spendera mindre tid på att leta efter information som kan vara till nytta. Det som skiljer sig i denna studie från tidigare liknande studier är att rekommendationsmotorn är implementerad för att passa en verklig verksamhet. I jämförelse med andra studier är denna studie även baserad på data direkt från verksamhetens användare. Vissa liknande artefakter har blivit implementerade, men då är de ofta mer generella eller har använt sig av data som inte är relevant för domänen. Det är också vanligare att liknande rekommendationsmotorer använder sig av direkt användarfeedback för att göra rekommendationer, vilket inte används i denna studie. / Studies regarding recommendation engines have gained greater importance in our reality of the digital community. With regards to the continuously growing amount of digital information it has become harder to find information that’s of importance to the individual. Some specific domains with enforcement of recommendation engines are more studied than others, domains that distribute services or items usually end up in this category. Other domains that are in need of recommendation engines, that’s not as well explored is business which enables learning through the internet. One of these business is called Nomp and provides a learning tool for kids and young teenagers in mathematics. The goal with this study is therefore to implement a recommendation engine for a business that is within this lesser explored domain. The goal is also to explore the advantages a recommendation engine would provide for its users. The study is based on a framework within design science research, which included various kinds of experiments and a survey. The results from these activities represented the empirics for the analysis that was conducted. The results show some signs that it’s possible to implement an artifact for this domain. However, it does not clearly show to what extent it’s valuable for the end user. For some part, the objectives for this study was met. Although, the advantages for the users could have been explored in greater depth. The overall prospects by conducting this study is that it will have some practical consequences, that the user can or will spend lesser time to search for important information. Differences between this study and other similar studies is that the recommendation engine is implemented to fit the needs of a real business. Also, compared to others, this study is based on data collected directly from the end users. Some similar systems have been implemented but the artefact is often more general or might have used data that’s not relevant the domain. It’s also more common that similar recommendation engines are using direct user feedback to make recommendations, which is not used in this study.

Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2014-12-18T18:42:03Z No. of bitstreams: 2 Dissertacao - Edjalma Queiroz da Silva - 2014.pdf: 3244366 bytes, checksum: 7c2506e59c6f1ebdc4608a6ff2ac207d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2014-12-22T11:15:25Z (GMT) No. of bitstreams: 2 Dissertacao - Edjalma Queiroz da Silva - 2014.pdf: 3244366 bytes, checksum: 7c2506e59c6f1ebdc4608a6ff2ac207d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-12-22T11:15:25Z (GMT). No. of bitstreams: 2 Dissertacao - Edjalma Queiroz da Silva - 2014.pdf: 3244366 bytes, checksum: 7c2506e59c6f1ebdc4608a6ff2ac207d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-08-20 / Recommendation systems function as a guide, helping users to discover products of interest. There are various techniques and approaches in the literature that enable the generationofrecommendations.Thisisinterestingbecauseitemphasizesthediversityof options;ontheotherhand,itcancausedoubtthesystemdesigneraboutwhichisthebest techniquetouse.Eachoftheseapproacheshasparticularitiesanddependsonthecontext to be applied. Therefore, the decision to choose between the techniques is complex to be done manually. This work proposes an evolutionary approach for combining results of recommendation techniques (Invenire) in order to automate the choice of techniques and get fewer errors in recommendations. To evaluate the proposal, experiments were performed with a dataset from MovieLens and some Collaborative Filtering techniques. The results show that the combining methodology proposed in this paper performs better than any one collaborative filtering technique separately in the context addressed. The improvement varies from 3,6% to 118,99% depending on the technique and the experiment executed. / Sistemas de Recomendação funcionam como um conselheiro, comportando-se de tal formaaorientaraspessoasnadescobertadeprodutosdeinteresse.Existemváriastécnicas eabordagensnaliteraturaquepermitemgerarrecomendações.Issoéinteressanteporque enfatiza a diversidade de opções; por outro lado, pode causar dúvida para o projetista do sistema sobre qual é a melhor técnica para usar. Cada uma destas abordagens tem particularidades e dependem do contexto para serem aplicadas. Assim, a decisão de escolher entre técnicas se torna complexa para ser feita manualmente. Este trabalho propõe uma abordagem evolutiva para automatizar a busca pela melhor combinação de resultados de técnicas de Sistemas de Recomendação e produzir menos erros nas recomendações.Paraavaliaraproposta,foramrealizadosexperimentoscomumconjunto de dados daMovieLens e algumas das técnicas de Filtragem Colaborativa. Os resultados mostramqueametodologiadecombinação,propostanestetrabalho,temumdesempenho melhor do que qualquer uma das técnicas isoladas de filtragem colaborativa no contexto abordado.A melhora varia de3,6%a 118,99%dependendo da técnica e do experimento executado.

[pt] O surgimento da World Wide Web representou uma nova oportunidade de publicidade, disponível para qualquer empresa: A possibilidade de exposição global para uma grande audiência a um custo extremamente pequeno. Como conseqüência disso, surgiu toda uma nova indústria oferecendo serviços relacionados à publicidade de busca, na qual uma empresa anunciante paga por uma posição de destaque em listas de anúncios. A fim de manter a credibilidade e a participação de mercado do serviço que os veicula - por exemplo, uma máquina de busca - os anúncios devem ser exibidos apenas para os usuários que se interessem por eles, no que se chama de Publicidade Direcionada. Em virtude disso, surge a necessidade de se utilizar um sistema de recomendação que seja capaz de escolher que anúncios exibir para quais usuários. Nos sistemas de recomendação baseados em filtragem colaborativa, as preferências de outros usuários são utilizadas como atributos para um sistema de aprendizado, pois estas podem ser bastante detalhadas, gerando recomendações não só para os itens mais populares como também para nichos de itens. Neste trabalho, é desenvolvido um sistema de recomendação de anúncios que aplica Filtragem Colaborativa baseada em fatoração de matrizes ao problema de predição do Click- Through Rate, uma métrica em Publicidade Direcionada que expressa a relevância de um anúncio para os usuários que buscam por uma determinada palavra- chave. A fim de validar o método proposto de predição do Click-Through Rate, realizamos vários experimentos em um conjunto de dados sintéticos. Adicionalmente, o trabalho contribui para o projeto do LearnAds, um framework de recomendação de anúncios baseado em Aprendizado de Máquina. / [en] The emergence of the World Wide Web represented a new advertising opportunity available to any company: The possibility of global exposure to a large audience at a very small cost. As a result, a whole new industry has emerged by offering services related to search advertising, in which an advertiser pays for a prominent position in lists of ads. In order to maintain the credibility and market share of the service that conveys them - for example, a search engine - such ads must be displayed only to users who are interested in them, on what is called Targeted Advertising. Therefore, those services need to use a recommendation system that can choose which ads show to which users. Recommendation systems based on collaborative filtering use the preferences of other users as features to a learning system, since such preferences can be quite detailed, generating recommendations not only for the most popular items but also to item niches. In this work, we develop an ads recommendation system that applies Collaborative Filtering based on matrix factorization to the problem of predicting the Click-Through Rate, a Targeted Advertising metric that expresses the relevance of a particular ad for the users searching for a specific keyword. In order to validate the proposed method of Click-Through Rate prediction, we carry out several experiments on a synthetic data set. Additionally, the work contributes to the design of LearnAds, a framework for ads recommendation systems based on Machine Learning.

[pt] Com a quantidade de informação e sua disponibilidade facilitada pelo uso da Internet, diversas opções são oferecidas às pessoas e estas, normalmente, possuem pouca ou quase nenhuma experiência para decidir dentre as alternativas existentes. Neste âmbito, os Sistemas de Recomendação surgem para organizar e recomendar automaticamente, através de Aprendizado de Máquina, itens interessantes aos usuários. Um dos grandes desafios deste tipo de sistema é realizar o casamento correto entre o que está sendo recomendado e aqueles que estão recebendo a recomendação. Este trabalho aborda um Sistema de Recomendação baseado em Filtragem Colaborativa, técnica cuja essência está na troca de experiências entre usuários com interesses comuns. Na Filtragem Colaborativa, os usuários pontuam cada item experimentado de forma a indicar sua relevância, permitindo que outros do mesmo grupo se beneficiem destas pontuações. Nosso objetivo é utilizar um algoritmo de Boosting para otimizar a performance dos Sistemas de Recomendação. Para isto, utilizamos uma base de dados de anúncios com fins de validação e uma base de dados de filmes com fins de teste. Após adaptações nas estratégias convencionais de Boosting, alcançamos melhorias de até 3% sobre a performance do algoritmo original. / [en] With the amount of information and its easy availability on the Internet, many options are offered to the people and they, normally, have little or almost no experience to decide between the existing alternatives. In this scene, the Recommendation Systems appear to organize and recommend automatically, through Machine Learning, the interesting items. One of the great recommendation challenges is to match correctly what is being recommended and who are receiving the recommendation. This work presents a Recommendation System based on Collaborative Filtering, technique whose essence is the exchange of experiences between users with common interests. In Collaborative Filtering, users rate each experimented item indicating its relevance allowing the use of ratings by other users of the same group. Our objective is to implement a Boosting algorithm in order to optimize a Recommendation System performance. For this, we use a database of advertisements with validation purposes and a database of movies with testing purposes. After adaptations in the conventional Boosting strategies, improvements of 3% were reached over the original algorithm.

In recent years, web has experienced a tremendous growth concerning users and content. As a result information overload problem has always been always one of the main discussion topics. The aim has always been to find the most desired solution in order to help users when they find it increasingly difficult to locate the accurate information at the right time. Recommender systems developed to address this need by helping users to find relevant information among huge amounts of data and they have now become a ubiquitous attribute to many websites. A recommender system guides users in their decisions by predicting their preferences while they are searching, shopping or generally surfing, based on their preferences collected from past as well as the preferences of other users. Until now, recommender systems has been vastly used in almost all professional e-commerce websites, selling or offering different variety of items from movies and music to clothes and foods. This thesis will present and explore different recommender system algorithms such as User-User Collaborative and Item-Item Collaborative filtering using open source library Apache mahout. Algorithms will be developed in order to evaluate the performance of these collaborative filtering algorithms. They will be compared and their performance will be measured in detail by using evaluation metrics such as RMSE and MAE and similarity algorithms such as Pearson and Loglikelihood.

Social networking has encouraged users to find new ways to create, post, search, collaborate and share information of various forms. Unfortunately there is a lot of data in social networks that is not well-managed, which makes the experience within these networks less than optimal. Therefore people generally need more and more time as well as advanced tools that are used for seeking relevant information. A new search paradigm is emerging, where the user perspective is completely reversed: from finding to being found. The aim of present thesis research is to evaluate two approaches of identifying content of interest: user-initiated and system-initiated. The most suitable approaches will be implemented. Various recommendation systems for system-initiated content recommendations will also be investigated, and the best suited ones implemented. The analysis that was performed demonstrated that the users have used all of the implemented approaches and have provided positive and negative comments for all of them, which reinforces the belief that the methods for the implementation were selected correctly. The results of the user testing of the methods were evaluated based on the amount of time it took the users to find the desirable content and on the correspondence of the result compared to the user expectations.