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A Singular Value Decomposition Approach For Recommendation SystemsOsmanli, Osman Nuri 01 July 2010 (has links) (PDF)
Data analysis has become a very important area for both companies and researchers as a consequence of the technological developments in recent years. Companies are trying to increase their profit by analyzing the existing data about their customers and making decisions for the future according to the results of these analyses. Parallel to the need of companies, researchers are investigating different methodologies to analyze data more accurately with high performance.
Recommender systems are one of the most popular and widespread data analysis tools. A recommender system applies knowledge discovery techniques to the existing data and makes personalized product recommendations during live customer interaction. However, the huge growth of customers and products especially on the internet, poses some challenges for recommender systems, producing high quality recommendations and performing millions of recommendations per second.
In order to improve the performance of recommender systems, researchers have proposed many different methods. Singular Value Decomposition (SVD) technique based on dimension reduction is one of these methods which produces high quality recommendations, but has to undergo very expensive matrix calculations. In this thesis, we propose and experimentally validate some contributions to SVD technique which are based on the user and the item categorization. Besides, we adopt tags to classical 2D (User-Item) SVD technique and report the results of experiments. Results are promising to make more accurate and scalable recommender systems.
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A Hybrid Veideo Recommendation System Based On A Graph Based AlgorithmOzturk, Gizem 01 September 2010 (has links) (PDF)
This thesis proposes the design, development and evaluation of a hybrid video
recommendation system. The proposed hybrid video recommendation system is based
on a graph algorithm called Adsorption. Adsorption is a collaborative filtering algorithm
in which relations between users are used to make recommendations. Adsorption is used
to generate the base recommendation list. In order to overcome the problems that occur
in pure collaborative system, content based filtering is injected. Content based filtering
uses the idea of suggesting similar items that matches user preferences. In order to use
content based filtering, first, the base recommendation list is updated by removing weak
recommendations. Following this, item similarities of the remaining list are calculated
and new items are inserted to form the final recommendations. Thus, collaborative
recommendations are empowered considering item similarities. Therefore, the
developed hybrid system combines both collaborative and content based approaches to
produce more effective suggestions.
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Discovering Roles In The Evolution Of Collaboration NetworksBharath Kumar, M 10 1900 (has links)
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.
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Using machine learning techniques to simplify mobile interfacesSigman, Matthew Stephen 19 April 2013 (has links)
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
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Local approaches for collaborative filteringLee, Joonseok 21 September 2015 (has links)
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.
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Harnessing the power of "favorites" lists for recommendation systemsKhezrzadeh, Maryam 08 January 2010 (has links)
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.
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Rekommendationsmotor: med fokus inom E-lärande / Recommendation engine: focus within E-learningJakobsson, Lennart, Nilsson, Thires January 2018 (has links)
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.
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Invenire: um método evolucionário para combinar resultados das técnicas de sistemas de recomendação baseado em filtragem colaborativa / Invenire: an evolutionary approach for combining results of recommender systems techniques based on collaborative filteringSilva, Edjalma Queiroz da 20 August 2014 (has links)
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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.
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[en] COLLABORATIVE FILTERING APPLIED TO TARGETED ADVERTISING / [pt] FILTRAGEM COLABORATIVA APLICADA A PUBLICIDADE DIRECIONADAROBERTO PEREIRA CAVALCANTE 27 October 2008 (has links)
[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.
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[en] BOOSTING FOR RECOMMENDATION SYSTEMS / [pt] BOOSTING PARA SISTEMAS DE RECOMENDAÇÃOTULIO JORGE DE A N DE S ANIBOLETE 02 April 2009 (has links)
[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.
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