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FinPathlight: Framework for an Ontology-Based, Multiagent, Hybrid Recommender System Designed to Increase Consumer Financial CapabilityBunnell, Lawrence 01 January 2019 (has links)
This study is a design science research (DSR) project in which a description of the development and evaluation process for several novel technological artifacts will be communicated. Specifically, this study will establish: 1) an ontology of recommender systems issues, 2) an ontology of financial capability goals, and 3) a framework for a Personal Financial Recommender System (PFRS) application designed to improve user financial capability, called FinPathlight. The impetus for the RecSys Issues Ontology is to address a gap in the literature by providing researchers with a comprehensive knowledge classification of the issues and limitations inherent to recommender systems research. The development of a Financial Capability Goals Ontology will contribute domain knowledge classification for technological systems within the domain of finance and serves as a recommendation item knowledgebase for our PFRS. The FinPathlight framework provides the architecture and principles of implementation for a novel, financial-technology (FinTech) PFRS. FinPathlight is designed to improve the financial capability of its users through the recommendation, tracking and assistance with achieving financial capability enhancing goals.
This research is notable in that it expands the influence and furthers the relevance of information systems research by providing an explicitly applicable research solution to an area of significant socio-economic importance, financial capability, a heretofore unsolved “wicked problem” (Churchman 1967) domain. In light of current financial conditions, recommender systems research that addresses a problem such as consumer financial capability is a step towards ensuring that information systems research continues to matter and retain its influence and relevance in everyday practice.
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A Hybrid Movie Recommender Using Dynamic Fuzzy ClusteringGurcan, Fatih 01 March 2010 (has links) (PDF)
Recommender systems are information retrieval tools helping users in their information
seeking tasks and guiding them in a large space of possible options. Many hybrid
recommender systems are proposed so far to overcome shortcomings born of pure
content-based (PCB) and pure collaborative filtering (PCF) systems. Most studies on
recommender systems aim to improve the accuracy and efficiency of predictions. In
this thesis, we propose an online hybrid recommender strategy (CBCFdfc) based on
content boosted collaborative filtering algorithm which aims to improve the prediction
accuracy and efficiency. CBCFdfc combines content-based and collaborative characteristics
to solve problems like sparsity, new item and over-specialization. CBCFdfc uses
fuzzy clustering to keep a certain level of prediction accuracy while decreasing online
prediction time. We compare CBCFdfc with PCB and PCF according to prediction
accuracy metrics, and with CBCFonl (online CBCF without clustering) according to
online recommendation time. Test results showed that CBCFdfc performs better than
other approaches in most cases. We, also, evaluate the effect of user-specified parameters
to the prediction accuracy and efficiency. According to test results, we determine
optimal values for these parameters. In addition to experiments made on simulated
data, we also perform a user study and evaluate opinions of users about recommended movies. The results that are obtained in user evaluation are satisfactory. As a result,
the proposed system can be regarded as an accurate and efficient hybrid online movie
recommender.
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Enhancing Accuracy Of Hybrid Recommender Systems Through Adapting The Domain TrendsAksel, Fatih 01 September 2010 (has links) (PDF)
Traditional hybrid recommender systems typically follow a manually created fixed prediction strategy in their decision making process. Experts usually design these static strategies as fixed combinations of different techniques. However, people' / s tastes and desires are temporary and they gradually evolve. Moreover, each domain has unique characteristics, trends and unique user interests. Recent research has mostly focused on static hybridization schemes which do not change at runtime. In this thesis work, we describe an adaptive hybrid recommender system, called AdaRec that modifies its attached prediction strategy at runtime according to the performance of prediction techniques (user feedbacks). Our approach to this problem is to use adaptive prediction strategies. Experiment results with datasets show that our system outperforms naive hybrid recommender.
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Evaluating Cold-Start in Recommendation Systems Using a Hybrid Model Based on Factorization Machines and SBERT Embeddings / Evaluering av kallstartsproblemet hos rekommendationssystem med en NLP-baserad hybridmodell baserad på faktoriseringsmaskiner och SBERT inbäddningarChowdhury, Sabrina January 2022 (has links)
The item cold-start problem, which describes the difficulty of recommendation systems in recommending new items to users, remains a great challenge for recommendation systems that rely on past user-item interaction data. A popular technique in the current research surrounding the cold-start problem is the use of hybrid models that combine two or more recommendation strategies that may contribute with their individual advantages. This thesis investigates the use of a hybrid model which combines Sentence BERT embeddings with a recommendation model based on Factorization Machines (FM). The research question is stated as: How does a hybrid recommendation system based on Factorization Machines with frozen Sentence BERT embeddings perform in terms of solving the cold-start problem?. Three experiments were conducted to answer the research question. These involved finding an optimal pre-trained Sentence BERT model, investigating the difference in performance between an FM-model and a hybrid FM-model, as well as the difference in ranking of an item depending on whether or not the hybrid FM-model has been trained on the item. The results show that the best pre-trained Sentence BERT model for producing meaningful embeddings is the paraphrase-MiniLM-L3-v2 model, that a hybrid FM-model and a standard FM-model perform almost equally in terms of precision and recall at 50, and that there is a weak correlation between the item-frequency and how the hybrid FM-model ranks an item when trained and not trained on the item. The answer to the research question is that a recommendation model based on Factorization Machines with frozen Sentence BERT embeddings displays low precision at 50 and recall at 50 values with the given parameters in comparison to the values given in an optimal recommendation scenario. The hybrid FM-model shows cold-start potential due to displaying similar results to the standard FM-model, but these values are so low that further investigation with other parameters is needed for a clearer conclusion. / Kallstartsproblem för artiklar som beskriver svårigheten hos rekommendationssystem gällande uppgiften att rekommendera nya artiklar till användare, är fortsatt en stor utmaning för rekommendationssystem som förlitar sig på data som beskriver interaktioner mellan användare och artiklar. En populär teknik inom den aktuella forskningen gällande kallstartsproblemet är användandet av hybridmodeller som kombinerar två eller flera rekommendationsstrategier och som potentiellt kan bidra med sina individuella fördelar. Detta examensarbete undersöker användandet av en hybridmodell som kombinerar menings-BERT inbäddningar med en rekommendationsmodell baserad på faktoriseringsmaskiner (FM). Frågeställningen lyder: Hur väl kan kallstartsproblemet för rekommendationer lösas med en hybridmodell baserad på faktoriseringsmaskiner med frusna menings-BERT-inbäddningar?. Tre experiment utfördes för att svara på frågeställningen. Dessa experiment innebar att hitta en optimal förtränad menings-BERT-modell, undersöka skillnaden i utförandet mellan en FM-modell och en hybrid FM-modell, samt skillnaden i ranking av en artikel baserat på huruvida hybridmodellen tränats eller inte tränats på artikeln. Resultaten visar att den bästa förtränade menings-BERT-modellen gällande skapandet av meningsfulla inbäddningar är paraphrase-MiniLM-L3-v2-modellen, att en hybrid FM-modell och en FM-modell genererar nästan identiska resultat baserat på precision och återkallelse för de första 50 resultaten och att det finns en svag korrelation mellan artikel-frekvens och hur hybridmodellen rankar en artikel när hybridmodellen tränats eller inte tränats på artikeln. Svaret på frågeställningen är att en hybrid FM-modell med frusna menings-BERT-inbäddningar visar låga resultat för precision och återkallelse för de första 50 resultaten givet de använda parametrarna jämfört med de värden som skulle genererats i ett optimalt rekommendationsscenario. Den hybrida FM-modellen visar kallstartspotential då den visar liknande resultat som FM-modellen, men dessa värden är så låga att frågan behöver undersökas ytterligare för tydligare resultat.
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Uma abordagem híbrida para sistemas de recomendação de notícias / A hybrid approach to news recommendation systemsPagnossim, José Luiz Maturana 09 April 2018 (has links)
Sistemas de Recomendação (SR) são softwares capazes de sugerir itens aos usuários com base no histórico de interações de usuários ou por meio de métricas de similaridade que podem ser comparadas por item, usuário ou ambos. Existem diferentes tipos de SR e dentre os que despertam maior interesse deste trabalho estão: SR baseados em conteúdo; SR baseados em conhecimento; e SR baseado em filtro colaborativo. Alcançar resultados adequados às expectativas dos usuários não é uma meta simples devido à subjetividade inerente ao comportamento humano, para isso, SR precisam de soluções eficientes e eficazes para: modelagem dos dados que suportarão a recomendação; recuperação da informação que descrevem os dados; combinação dessas informações dentro de métricas de similaridade, popularidade ou adequabilidade; criação de modelos descritivos dos itens sob recomendação; e evolução da inteligência do sistema de forma que ele seja capaz de aprender a partir da interação com o usuário. A tomada de decisão por um sistema de recomendação é uma tarefa complexa que pode ser implementada a partir da visão de áreas como inteligência artificial e mineração de dados. Dentro da área de inteligência artificial há estudos referentes ao método de raciocínio baseado em casos e da recomendação baseada em casos. No que diz respeito à área de mineração de dados, os SR podem ser construídos a partir de modelos descritivos e realizar tratamento de dados textuais, constituindo formas de criar elementos para compor uma recomendação. Uma forma de minimizar os pontos fracos de uma abordagem, é a adoção de aspectos baseados em uma abordagem híbrida, que neste trabalho considera-se: tirar proveito dos diferentes tipos de SR; usar técnicas de resolução de problemas; e combinar recursos provenientes das diferentes fontes para compor uma métrica unificada a ser usada para ranquear a recomendação por relevância. Dentre as áreas de aplicação dos SR, destaca-se a recomendação de notícias, sendo utilizada por um público heterogêneo, amplo e exigente por relevância. Neste contexto, a presente pesquisa apresenta uma abordagem híbrida para recomendação de notícias construída por meio de uma arquitetura implementada para provar os conceitos de um sistema de recomendação. Esta arquitetura foi validada por meio da utilização de um corpus de notícias e pela realização de um experimento online. Por meio do experimento foi possível observar a capacidade da arquitetura em relação aos requisitos de um sistema de recomendação de notícias e também confirmar a hipótese no que se refere à privilegiar recomendações com base em similaridade, popularidade, diversidade, novidade e serendipidade. Foi observado também uma evolução nos indicadores de leitura, curtida, aceite e serendipidade conforme o sistema foi acumulando histórico de preferências e soluções. Por meio da análise da métrica unificada para ranqueamento foi possível confirmar sua eficácia ao verificar que as notícias melhores colocadas no ranqueamento foram as mais aceitas pelos usuários / Recommendation Systems (RS) are software capable of suggesting items to users based on the history of user interactions or by similarity metrics that can be compared by item, user, or both. There are different types of RS and those which most interest in this work are content-based, knowledge-based and collaborative filtering. Achieving adequate results to user\'s expectations is a hard goal due to the inherent subjectivity of human behavior, thus, the RS need efficient and effective solutions to: modeling the data that will support the recommendation; the information retrieval that describes the data; combining this information within similarity, popularity or suitability metrics; creation of descriptive models of the items under recommendation; and evolution of the systems intelligence to learn from the user\'s interaction. Decision-making by a RS is a complex task that can be implemented according to the view of fields such as artificial intelligence and data mining. In the artificial intelligence field there are studies concerning the method of case-based reasoning that works with the principle that if something worked in the past, it may work again in a new similar situation the one in the past. The case-based recommendation works with structured items, represented by a set of attributes and their respective values (within a ``case\'\' model), providing known and adapted solutions. Data mining area can build descriptive models to RS and also handle, manipulate and analyze textual data, constituting one option to create elements to compose a recommendation. One way to minimize the weaknesses of an approach is to adopt aspects based on a hybrid solution, which in this work considers: taking advantage of the different types of RS; using problem-solving techniques; and combining resources from different sources to compose a unified metric to be used to rank the recommendation by relevance. Among the RS application areas, news recommendation stands out, being used by a heterogeneous public, ample and demanding by relevance. In this context, the this work shows a hybrid approach to news recommendations built through a architecture implemented to prove the concepts of a recommendation system. This architecture has been validated by using a news corpus and by performing an online experiment. Through the experiment it was possible to observe the architecture capacity related to the requirements of a news recommendation system and architecture also related to privilege recommendations based on similarity, popularity, diversity, novelty and serendipity. It was also observed an evolution in the indicators of reading, likes, acceptance and serendipity as the system accumulated a history of preferences and solutions. Through the analysis of the unified metric for ranking, it was possible to confirm its efficacy when verifying that the best classified news in the ranking was the most accepted by the users
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Uma abordagem híbrida para sistemas de recomendação de notícias / A hybrid approach to news recommendation systemsJosé Luiz Maturana Pagnossim 09 April 2018 (has links)
Sistemas de Recomendação (SR) são softwares capazes de sugerir itens aos usuários com base no histórico de interações de usuários ou por meio de métricas de similaridade que podem ser comparadas por item, usuário ou ambos. Existem diferentes tipos de SR e dentre os que despertam maior interesse deste trabalho estão: SR baseados em conteúdo; SR baseados em conhecimento; e SR baseado em filtro colaborativo. Alcançar resultados adequados às expectativas dos usuários não é uma meta simples devido à subjetividade inerente ao comportamento humano, para isso, SR precisam de soluções eficientes e eficazes para: modelagem dos dados que suportarão a recomendação; recuperação da informação que descrevem os dados; combinação dessas informações dentro de métricas de similaridade, popularidade ou adequabilidade; criação de modelos descritivos dos itens sob recomendação; e evolução da inteligência do sistema de forma que ele seja capaz de aprender a partir da interação com o usuário. A tomada de decisão por um sistema de recomendação é uma tarefa complexa que pode ser implementada a partir da visão de áreas como inteligência artificial e mineração de dados. Dentro da área de inteligência artificial há estudos referentes ao método de raciocínio baseado em casos e da recomendação baseada em casos. No que diz respeito à área de mineração de dados, os SR podem ser construídos a partir de modelos descritivos e realizar tratamento de dados textuais, constituindo formas de criar elementos para compor uma recomendação. Uma forma de minimizar os pontos fracos de uma abordagem, é a adoção de aspectos baseados em uma abordagem híbrida, que neste trabalho considera-se: tirar proveito dos diferentes tipos de SR; usar técnicas de resolução de problemas; e combinar recursos provenientes das diferentes fontes para compor uma métrica unificada a ser usada para ranquear a recomendação por relevância. Dentre as áreas de aplicação dos SR, destaca-se a recomendação de notícias, sendo utilizada por um público heterogêneo, amplo e exigente por relevância. Neste contexto, a presente pesquisa apresenta uma abordagem híbrida para recomendação de notícias construída por meio de uma arquitetura implementada para provar os conceitos de um sistema de recomendação. Esta arquitetura foi validada por meio da utilização de um corpus de notícias e pela realização de um experimento online. Por meio do experimento foi possível observar a capacidade da arquitetura em relação aos requisitos de um sistema de recomendação de notícias e também confirmar a hipótese no que se refere à privilegiar recomendações com base em similaridade, popularidade, diversidade, novidade e serendipidade. Foi observado também uma evolução nos indicadores de leitura, curtida, aceite e serendipidade conforme o sistema foi acumulando histórico de preferências e soluções. Por meio da análise da métrica unificada para ranqueamento foi possível confirmar sua eficácia ao verificar que as notícias melhores colocadas no ranqueamento foram as mais aceitas pelos usuários / Recommendation Systems (RS) are software capable of suggesting items to users based on the history of user interactions or by similarity metrics that can be compared by item, user, or both. There are different types of RS and those which most interest in this work are content-based, knowledge-based and collaborative filtering. Achieving adequate results to user\'s expectations is a hard goal due to the inherent subjectivity of human behavior, thus, the RS need efficient and effective solutions to: modeling the data that will support the recommendation; the information retrieval that describes the data; combining this information within similarity, popularity or suitability metrics; creation of descriptive models of the items under recommendation; and evolution of the systems intelligence to learn from the user\'s interaction. Decision-making by a RS is a complex task that can be implemented according to the view of fields such as artificial intelligence and data mining. In the artificial intelligence field there are studies concerning the method of case-based reasoning that works with the principle that if something worked in the past, it may work again in a new similar situation the one in the past. The case-based recommendation works with structured items, represented by a set of attributes and their respective values (within a ``case\'\' model), providing known and adapted solutions. Data mining area can build descriptive models to RS and also handle, manipulate and analyze textual data, constituting one option to create elements to compose a recommendation. One way to minimize the weaknesses of an approach is to adopt aspects based on a hybrid solution, which in this work considers: taking advantage of the different types of RS; using problem-solving techniques; and combining resources from different sources to compose a unified metric to be used to rank the recommendation by relevance. Among the RS application areas, news recommendation stands out, being used by a heterogeneous public, ample and demanding by relevance. In this context, the this work shows a hybrid approach to news recommendations built through a architecture implemented to prove the concepts of a recommendation system. This architecture has been validated by using a news corpus and by performing an online experiment. Through the experiment it was possible to observe the architecture capacity related to the requirements of a news recommendation system and architecture also related to privilege recommendations based on similarity, popularity, diversity, novelty and serendipity. It was also observed an evolution in the indicators of reading, likes, acceptance and serendipity as the system accumulated a history of preferences and solutions. Through the analysis of the unified metric for ranking, it was possible to confirm its efficacy when verifying that the best classified news in the ranking was the most accepted by the users
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