In this thesis, a tag-based approach is proposed for predicting mashup patterns, thus deriving inspiration for potential new mashups from the community's consensus. The proposed approach applies association rule mining techniques to discover relationships between APIs and mashups based on their annotated tags. The importance of the mined relationships is advocated as a valuable source for recommending mashup candidates while mitigating common problems in recommender systems. The proposed methodology is evaluated through experimentation using a real-life dataset. Results show that the proposed mining approach achieves prediction accuracy with 60% precision and 79% recall improvement over a direct string matching approach that lacks the mining information. / Master of Science

Collaboration and sharing of information are the basis of modern social web system. Users in the social web systems are establishing and joining online communities, in order to collectively share their content with a group of people having common topic of interest. Group or community activities have increased exponentially in modern social Web systems. With the explosive growth of social communities, users of social Web systems have experienced considerable difficulty with discovering communities relevant to their interests. In this study, we address the problem of recommending communities to individual users. Recommender techniques that are based solely on community affiliation, may fail to find a wide range of proper communities for users when their available data are insufficient. We regard this problem as tag-based personalized searches. Based on social tags used by members of communities, we first represent communities in a low-dimensional space, the so-called latent semantic space, by using Latent Semantic Analysis. Then, for recommending communities to a given user, we capture how each community is relevant to both user’s personal tag usage and other community members’ tagging patterns in the latent space. We specially focus on the challenging problem of recommending communities to users who have joined very few communities or having no prior community membership. Our evaluation on two heterogeneous datasets shows that our approach can significantly improve the recommendation quality.

Recommender systems are ubiquitously used by online vendors as profitable tools to boost sales and enhance the purchase experience of their consumers. In recent literature, the value created by recommender systems are discussed extensively. In contrast, few researchers look at the negative side of the recommender systems from the viewpoint of policymakers. To fill this gap, I critically investigate the welfare impact of recommender systems (RSs) during my Ph.D. study. The main focus of my Ph.D. dissertation is analyzing whether there exists a conflict of interest between the recommendations provider and its consumers in the electronic marketplace. My dissertation is composed of three parts. In Part I, I evaluate empirically whether in the real world, the profit-driven firm will choose a recommendation mechanism that hurts or is suboptimal to its consumers. In Part II, I analyze the role of personalization technology in the RSs from a unique perspective of how personalization resembles price discrimination as a profitable tool to exploit consumer surplus. In part III, I investigate the vendor’s motivation to increase the level of personalization in two-period transactions. As the RSs are designed by the firm, and the firm’s objective is to maximize profits, the RSs might not maximize consumers’ welfare. In Part I of my thesis work, I test the existence of such a conflict of interest between the firm and its consumers. I explore this question empirically with a concrete RS created by our industry collaborator for their Video-on-Demand (VoD) system. Using a large-scale dataset (300,000 users) from a randomized experiment on the VoD platform, I simulate seven RSs based on an exponential demand model with listed movie orders and prices as key inputs, estimated from the experimental dataset. The seven simulated RSs differ by the assignments of listed orders for selected recommended movies. Specifically, assignments are chosen to maximize profits, consumer surplus, social welfare, popularity (IMDB votes and IMDB ratings), and previous sales, as well as random assignments. As a result, the profit-driven recommender system generates 8% less consumer surplus than the consumer-driven RSs, providing evidence for a conflict of interest between the vendor and its consumers. Major e-vendors personalize recommendations by different algorithms that depend on how much and types of consumer information obtained. Therefore, the welfare evaluations of personalized recommendation strategies by empirical methods are hard to generalize. In Part II of my thesis, I base my analysis of personalization in RSs on a conceptual approach. Under an analytic framework of horizontal product differentiation and heterogenous consumer preferences, the resemblance of personalization to price discrimination in welfare properties is presented. Personalization is beneficial to consumers when more personalization leads to more adoption of recommendations, since it decreases search costs for more consumers. However, when the level surpasses a threshold when all consumers adopt, a more personalized RS decreases consumer surplus and only helps the firm to exploit surplus from consumers. The extreme case of perfect personalization generates the same welfare results as first-degree price discrimination where consumers get perfectly fit recommendations but are charged their willingness-to-pay. As shown in Part II, personalization is always profitable for the monopoly seller. In Part III, I investigate the vendor’s motivation to increase the level of personalization in a two-period transactions. In the first period, consumers do not observe the true quality of the recommendations and choose to accept recommended products or not based on their initial guesses. In the second period, consumers fully learn the quality. The settings of consumer uncertainty and consumer learning incentivize the firm to charge lower-than-exploiting price for recommendations to ensure consumers’ first-period adoptions of the RS. Therefore, uncertainties mediate the conflicts of interest from the vendor’s exploitive behavior even though the vendor might strategically elevate consumers’ initial evaluation to reduce such effect.

Sistemas de Recomendação ajudam na personalização de sites na Internet oferecendo conteúdo ou produtos específicos aos usuários. Com dispositivos móveis, aumentou o interesse do usuário em ter recomendações personalizadas de locais para ir de acordo com o seu histórico de navegação e avaliações como restaurantes e pontos turísticos. Para que as recomendações personalizadas por locais sejam mais precisas é necessário contextualizá-las de acordo com o interesse do usuário que caracterizado por locais que ele visitou e por regiões de interesse como moradia, onde trabalha ou mesmo onde passará férias. Várias técnicas de contextualização utilizaram todos os locais que o usuário visitou para geração da recomendação contextual do local, outras técnicas trabalham na arquitetura híbrida. Muitas assumem que é necessário a posição exata do usuário para que as recomendações sejam online, o que muitas vezes não é possível por limitações técnicas ou mesmo indisponibilização do usuário por questões de privacidade. O objetivo principal deste trabalho é geração de recomendações usando multicontextos de forma offline, gerando vários contextos de cada usuário. Os locais são recomendados utilizando apenas dados históricos, sem a localização exata no usuário do momento da recomendação. Para atingir este objetivo foram utilizadas técnicas de clustering para mapeamento e divisão dos contextos em regiões indicando o interesse do usuário gerando a recomendação final dos locais a partir de um método híbrido de recomendação que usa filtragem colaborativa e a recomendação contextual proposta. Os resultados mostraram que a técnica proposta apresenta recomendações melhores do que apenas a recomendação colaborativa pura e, para usuários assíduos, as recomendações são melhores do que as técnicas base usadas para comparação. / Recommender Systems help in web sites personalization, offering specific content or products to users. With mobile devices, user interest in Point-of-Interest (POI) recommendation has increased to receive recommendations about places to go according to your navigation and evaluation history in the web site. POI recommendation are improved by contextualizing according to users interest, based on places to where user has been and on regions of interest such as the region where the user lives, works or the region intends go on vacation. Many contextualization techniques use all places that user visited for generation of POI contextual recommendation. Other techniques use hybrid architectures and many of them assume that is necessary the exact point where the user is for online recommendation and this in not possible always due technical limitations or user privacy. The main objective of this work is the offline generation of recommendations using multicontexts. Places to be recommended use only user historical data, without the user current localization at the moment of recommendation. Several techniques have been used for clustering for mapping and division of contexts in regions, indicating the user interests and, finally, generating the final recommendation using a hybrid method with collaborative filtering and contextual recommendation proposed. The results indicate that the proposed technique builds better recommendations than the pure collaborative filtering technique and for heavy users the proposed technique has better results the baseline technique used for comparison.

Sistemas de Recomendação ajudam na personalização de sites na Internet oferecendo conteúdo ou produtos específicos aos usuários. Com dispositivos móveis, aumentou o interesse do usuário em ter recomendações personalizadas de locais para ir de acordo com o seu histórico de navegação e avaliações como restaurantes e pontos turísticos. Para que as recomendações personalizadas por locais sejam mais precisas é necessário contextualizá-las de acordo com o interesse do usuário que caracterizado por locais que ele visitou e por regiões de interesse como moradia, onde trabalha ou mesmo onde passará férias. Várias técnicas de contextualização utilizaram todos os locais que o usuário visitou para geração da recomendação contextual do local, outras técnicas trabalham na arquitetura híbrida. Muitas assumem que é necessário a posição exata do usuário para que as recomendações sejam online, o que muitas vezes não é possível por limitações técnicas ou mesmo indisponibilização do usuário por questões de privacidade. O objetivo principal deste trabalho é geração de recomendações usando multicontextos de forma offline, gerando vários contextos de cada usuário. Os locais são recomendados utilizando apenas dados históricos, sem a localização exata no usuário do momento da recomendação. Para atingir este objetivo foram utilizadas técnicas de clustering para mapeamento e divisão dos contextos em regiões indicando o interesse do usuário gerando a recomendação final dos locais a partir de um método híbrido de recomendação que usa filtragem colaborativa e a recomendação contextual proposta. Os resultados mostraram que a técnica proposta apresenta recomendações melhores do que apenas a recomendação colaborativa pura e, para usuários assíduos, as recomendações são melhores do que as técnicas base usadas para comparação. / Recommender Systems help in web sites personalization, offering specific content or products to users. With mobile devices, user interest in Point-of-Interest (POI) recommendation has increased to receive recommendations about places to go according to your navigation and evaluation history in the web site. POI recommendation are improved by contextualizing according to users interest, based on places to where user has been and on regions of interest such as the region where the user lives, works or the region intends go on vacation. Many contextualization techniques use all places that user visited for generation of POI contextual recommendation. Other techniques use hybrid architectures and many of them assume that is necessary the exact point where the user is for online recommendation and this in not possible always due technical limitations or user privacy. The main objective of this work is the offline generation of recommendations using multicontexts. Places to be recommended use only user historical data, without the user current localization at the moment of recommendation. Several techniques have been used for clustering for mapping and division of contexts in regions, indicating the user interests and, finally, generating the final recommendation using a hybrid method with collaborative filtering and contextual recommendation proposed. The results indicate that the proposed technique builds better recommendations than the pure collaborative filtering technique and for heavy users the proposed technique has better results the baseline technique used for comparison.

Collaboration and sharing of information are the basis of modern social web system. Users in the social web systems are establishing and joining online communities, in order to collectively share their content with a group of people having common topic of interest. Group or community activities have increased exponentially in modern social Web systems. With the explosive growth of social communities, users of social Web systems have experienced considerable difficulty with discovering communities relevant to their interests. In this study, we address the problem of recommending communities to individual users. Recommender techniques that are based solely on community affiliation, may fail to find a wide range of proper communities for users when their available data are insufficient. We regard this problem as tag-based personalized searches. Based on social tags used by members of communities, we first represent communities in a low-dimensional space, the so-called latent semantic space, by using Latent Semantic Analysis. Then, for recommending communities to a given user, we capture how each community is relevant to both user’s personal tag usage and other community members’ tagging patterns in the latent space. We specially focus on the challenging problem of recommending communities to users who have joined very few communities or having no prior community membership. Our evaluation on two heterogeneous datasets shows that our approach can significantly improve the recommendation quality.

An individual’s ability to locate the information they desire grows more slowly than the rate at which new information becomes available. Customers are constantly confronted with situations in which they have many options to choose from and need assistance exploring or narrowing down the possibilities. Recommender systems are one tool to help bridge this gap. There are various mechanisms being employed to create recommender systems, but the most common systems fall into two main classes: content-based and collaborative filtering systems. Content-based recommender systems match the textual information of a particular product with the textual information representing the interests of a customer. Collaborative filtering systems use patterns in customer ratings to make recommendations. Both types of recommender systems require significant data resources in the form of a customer’s ratings and product features; hence they are not able to generate high quality recommendations. Hybrid mechanisms have been used by researchers to improve the performance of recommender systems where one can integrate more than one mechanism to overcome the drawbacks of an individual system. The hybrid approach proposed in this thesis is the integration of content and context-based with collaborative filtering, since these are the most successful and widely used mechanisms. This proposed approach will look into the integration of content and context data with rating data using a different mechanism that mainly focuses on boosting a customer’s trust in the recommender system. Researchers have been trying to improve system performance using hybrid approaches, but research is lacking on providing justifications for recommended products. Hence, the proposed approach will mainly focus on providing justifications for recommended products as this plays a crucial role in obtaining the satisfaction and trust of customers. A product’s features and a customer’s context attributes are used to provide justifications. In addition to this, the presentation mechanism needs to be very effective as it has been observed that customers trust more in a system when there are explanations on how the recommended products have been computed and presented. Finally, this proposed recommender system will allow the customer to interact with it in various ways to provide feedback on the recommendations and justifications. Overall, this integration will be very useful in achieving a stronger correlation between the customers and products. Experimental results clearly showed that the majority of the participants prefer to have recommendations with their justifications and they received valuable recommendations on which they could trust.

In this dissertation, the issues of the increased awareness of energy use are considered. Energy technologies are continuously improved by energy retailers and academic researchers. The Smart Grid are soon customary as part of the energy domain. But in order to improve energy efficiency the change must come from the consumers. Consumers should be active decision makers in the Smart Grid domain and therefor a Recommender system suits the Smart Grid and enables customers. Customers will not use energy in the way energy retailers, and politicians advocates instead they will do what fits them. By investigating how a Recommender can be built in the Smart Grid we focus on parameters and information that supports the costumers and enables positive change. An investigation of what customers perceive as relevant is pursued as well as how relevancy can adjust the system. A conceptual model of how to build a Recommender is rendered through a literature review, a group interview and a questionnaire.

Thesis (M.A.). / Written for the Dept. of Art History and Communication Studies [Communications Graduate Program]. Title from title page of PDF (viewed 2008/05/12). Includes bibliographical references.

Made available in DSpace on 2014-06-12T15:59:57Z (GMT). No. of bitstreams: 2 arquivo5808_1.pdf: 2179927 bytes, checksum: f047e667d6364f038f0a1fdd91b757b2 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011 / A atividade de teste de software pode ser bastante árdua e custosa. No contexto de testes manuais, todo o esforço com o objetivo de reduzir o tempo de execução dos testes e aumentar a contenção de defeitos é bem-vindo. Uma possível estratégia é alocar os casos de teste de acordo com o perfil do testador de forma a maximizar a produtividade. Entretanto, otimizar a alocação de casos de teste não é uma tarefa trivial: em grandes companhias, gerentes de teste são responsáveis por alocar centenas de casos de teste aos testadores disponíveis ao início de uma nova execução. Neste trabalho nós propomos dois algoritmos para a alocação automática de casos de teste e três perfis para os testadores baseados em sistemas de recomendação (o mesmo tipo de sistema que recomenda, por exemplo, um livro na Amazon.com ou um filme no Netflix.com). Cada um dos algoritmos de alocação pode ser combinado com os três perfis de testador, resultando em seis sistemas de alocação possíveis: Exp-Manager, Exp-Blind, MO-Manager, MO-Blind, Eff-Manager, e Eff-Blind. Nossos sistemas de alocação consideram a efetividade (defeitos válidos encontrados no passado) e experiência do testador (habilidade em executar testes com determinadas características). Com o objetivo de comparar os nossos sistemas de alocação com a alocação do gerente e com alocações aleatórias, um experimento controlado, utilizando 100 alocações com pelo menos 50 casos de teste cada uma, foi realizado em um cenário industrial real. Os sistemas de alocação foram avaliados através das métricas de precisão, recall e taxa de não-alocação (percentual de casos de teste não alocados). Em nosso experimento, a aplicação da ANOVA (uma técnica estatística utilizada para verificar se as amostras de dois ou mais grupos são oriundas de populações com médias iguais) e do teste de Tukey (um procedimento de comparações múltiplas para identificar quais médias são significativamente diferentes entre si) mostraram que o Exp-Manager supera os demais sistemas de alocação com respeito às métricas de precisão e recall. Todos os sistemas de alocação mostraram-se superiores ao algoritmo randômico. A precisão média (entre os sistemas de alocação) variou de 39.32% a 64.83% enquanto o recall médio variou de 39.19% a 64.83%; para a métrica de não-alocação, três sistemas de alocação (Exp-Manager, Exp-Blind e MOBlind) apresentaram um melhor desempenho alcançando taxa zero de não-alocação para todas as alocações de testes. A taxa média de não-alocação variou de 0% a 2.34% (para a métrica não-alocação, quanto menor, melhor). No cenário industrial real onde o nosso trabalho foi realizado, gerentes de teste gastam de 16 a 30 dias de trabalho por ano com a atividade de alocação de casos de teste. Nossos sistemas de alocação podem ajudá-los a realizar esta atividade de forma mais rápida e mais eficaz