[pt] Sistemas de recomendação aparecem em diversos domínios do mundo real. Vários modelos foram propostos para o problema de predição de entradas faltantes em um conjunto de dados. Duas das abordagens mais comuns são filtragem colaborativa baseada em similaridade e modelos de fatores latentes. Uma alternativa, mais recente, foi proposta por Salakhutdinov em 2007, usando máquinas de Boltzmann restritas, ou RBMs. Esse modelo se encaixa na família de modelos de fatores latentes, no qual, modelamos fatores latentes dos dados usando unidades binárias na camada escondida das RBMs. Esses modelos se mostraram capazes de aproximar resultados obtidos com modelos de fatoração de matrizes. Nesse trabalho vamos revisitar esse modelo e detalhar cuidadosamente como modelar e treinar RBMs para o problema de predição de entradas vazias em dados tabulares. / [en] Recommender systems can be used in many problems in the real world. Many models were proposed to solve the problem of predicting missing entries in a specific dataset. Two of the most common approaches are neighborhood-based collaborative filtering and latent factor models. A more recent alternative was proposed on 2007 by Salakhutdinov, using Restricted Boltzmann Machines. This models belongs to the family of latent factor models, in which, we model latent factors over the data using hidden binary units. RBMs have shown that they can approximate solutions trained with a traditional matrix factorization model. In this work we ll revisit this proposed model and carefully detail how to model and train RBMs for the problem of missing ratings prediction.

Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics, and CD/DVDs, online shopping has surpassed or even replaced the traditional shopping method. Compared with traditional retailing, e-commerce is information intensive. One of the key factors to succeed in e-business is how to facilitate the consumers’ approaches to discover a product. Conventionally a product search engine based on a keyword search or category browser is provided to help users find the product information they need. The general goal of a product search system is to enable users to quickly locate information of interest and to minimize users’ efforts in search and navigation. In this process human factors play a significant role. Finding product information could be a tricky task and may require an intelligent use of search engines, and a non-trivial navigation of multilayer categories. Searching for useful product information can be frustrating for many users, especially those inexperienced users. This dissertation focuses on developing a new visual product search system that effectively extracts the properties of unstructured products, and presents the possible items of attraction to users so that the users can quickly locate the ones they would be most likely interested in. We designed and developed a feature extraction algorithm that retains product color and local pattern features, and the experimental evaluation on the benchmark dataset demonstrated that it is robust against common geometric and photometric visual distortions. Besides, instead of ignoring product text information, we investigated and developed a ranking model learned via a unified probabilistic hypergraph that is capable of capturing correlations among product visual content and textual content. Moreover, we proposed and designed a fuzzy hierarchical co-clustering algorithm for the collaborative filtering product recommendation. Via this method, users can be automatically grouped into different interest communities based on their behaviors. Then, a customized recommendation can be performed according to these implicitly detected relations. In summary, the developed search system performs much better in a visual unstructured product search when compared with state-of-art approaches. With the comprehensive ranking scheme and the collaborative filtering recommendation module, the user’s overhead in locating the information of value is reduced, and the user’s experience of seeking for useful product information is optimized.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-07-12T13:47:04Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) risethesis.pdf: 1393384 bytes, checksum: f5f2fb9182ce60a9c5d2b0cd95f2893a (MD5) / Made available in DSpace on 2017-07-12T13:47:04Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) risethesis.pdf: 1393384 bytes, checksum: f5f2fb9182ce60a9c5d2b0cd95f2893a (MD5) Previous issue date: 2016-06-29 / Em meio a grande sobrecarga de dados disponíveis na internet, sistemas de recomendação tornam-se ferramentas indispensáveis para auxiliar usuários no encontro de itens ou conteúdos relevantes. Diversas técnicas de recomendação são aplicadas em diversos tipos de domínios diferentes. Seja na recomendação de filmes, música, amigos, lugares ou notícias, sistemas de recomendação exploram diversas informações disponíveis para aprender as preferências dos usuários e promover recomendações úteis. Uma das estratégias mais utilizadas é a de filtragem colaborativa. A qualidade dessa estratégia depende da quantidade de avaliações disponíveis e da qualidade do algoritmo utilizado para predição de avaliação. Estudos recentes demonstram que informações provenientes de redes sociais podem ser muito úteis para aumentar a precisão das recomendações. Assim como acontece no mundo real, no mundo virtual usuários buscam recomendações e conselhos de amigos antes de comprar um item ou consumir algum serviço, informações desse tipo podem ser úteis para definição do contexto social da recomendação. Além do social, informações físicas e temporais passaram a ser utilizadas para definição do contexto físico de cada recomendação. A companhia, a localização e as condições climáticas são bons exemplos de elementos físicos que levam um usuário a preferir certos itens. Um processo de recomendação que não leve em consideração elementos contextuais pode fazer com que o usuário tenha uma péssima experiência consumindo determina do item recomendado equivocadamente. Esta dissertação tem como objetivo investigar técnicas de filtragem colaborativa que utilizam contexto a fim de realizar recomendações que auxiliem usuários no encontro de itens relevantes. Nesse tipo de técnica, um sistema de recomendação base é utilizando para fornecer recomendações para o usuário alvo. Em seguida, são filtrados apenas os itens considerados relevantes para contextos previamente identificados nas preferências do usuário alvo. As técnicas implementadas foram aplicadas em dois experimentos com duas bases de dados de domínios diferentes: uma base composta por eventos e outra por filmes. Na recomendação de eventos, investigamos o uso de contextos físicos (i.e., tempo e local) e de contextos sociais (i.e., amigos na rede social) associados aos itens sugeridos aos usuários. Na recomendação de filmes, por sua vez, investigamos novamente o uso de contexto social. A partir da aplicação de pós-filtragem em três algoritmos de filtragem colaborativa usados como base, foi possível recomendar itens de forma mais precisa, como demonstrado nos experimentos realizados. / The overload of data available on the internet makes recommendation systems become indispensable tools to assist users in meeting items or relevant content. Several recommendation techniques were has been userd in many different types of domains. Those systems can recommend movies, music, friends, places or news; recommender systems can exploit different information available to learn preferences of users and promote more useful recommendations. The collaborative filtering strategy is one of the most used. The quality of this technique depends on the number of available ratings and the algorithm used to predict. Recent studies show that information from social networks can be very useful to increase the accuracy recommendations. Just as in the real world, the virtual world users ask recommendations and advice from friends before buying an item or consume a service. Furthermore, the context of each rating may be crucial for the definition of new ratings. Location, date time and weather conditions are good examples of useful elements to define what should be the best items to recommend for some user. A recommendation process that does not respect those elements can provide a user a bad experience. This dissertation investigates collaborative filtering techniques based on context, and more specifically techniques based on post-filtering. First, a recommendation system was used to provide recommendations for a specific user. Then, only relevant items according to context preferences for the target user will be recommended. The techniques implemented was applied in two case studies with two different domains databases: one base composed of events and another of movies. In the event of recommendation, we investigated the use of physical contexts (i.e., time and place) and social contexts (i.e., friends in the social network) associated with items suggested to users. On the recommendation of movies, in turn, again we investigated the use of social context. From the application of post-filtering in three collaborative filtering algorithms used as a baseline, it was possible to recommend items more accurately, as demonstrated in the experiments.

Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation system

Recommender systems are widely used in many domains. In this work, the importance of a recommender system in an online learning platform is discussed. After explaining the concept of adding an intelligent agent to online education systems, some features of the Course Networking (CN) website are demonstrated. Finally, the relation between CN, the intelligent agent (Rumi), and the recommender system is presented. Along with the argument of three different approaches for building a community recommendation system. The result shows that the Neighboring Collaborative Filtering (NCF) outperforms both the transfer learning method and the Continuous bag-of-words approach. The NCF algorithm has a general format with two various implementations that can be used for other recommendations, such as course, skill, major, and book recommendations.

<div><div><div><p>As technological capacity to store and exchange information progress, the amount of available data grows explosively, which can lead to information overload. The dif- ficulty of making decisions effectively increases when one has too much information about that issue. Recommendation systems are a subclass of information filtering systems that aim to predict a user’s opinion or preference of topic or item, thereby providing personalized recommendations to users by exploiting historic data. They are widely used in e-commerce such as Amazon.com, online movie streaming com- panies such as Netflix, and social media networks such as Facebook. Memory-based collaborative filtering (CF) is one of the recommendation system methods used to predict a user’s rating or preference by exploring historic ratings, but without in- corporating any content information about users or items. Many studies have been conducted on memory-based CFs to improve prediction accuracy, but none of them have achieved better prediction accuracy than state-of-the-art model-based CFs. Fur- thermore, A product or service is not judged only by its own characteristics but also by the characteristics of other products or services offered concurrently. It can also be judged by anchoring based on users’ memories. Rating or satisfaction is viewed as a function of the discrepancy or contrast between expected and obtained outcomes documented as contrast effects. Thus, a rating given to an item by a user is a compar- ative opinion based on the user’s past experiences. Therefore, the score of ratings can be affected by the sequence and time of ratings. However, in traditional CFs, pairwise similarities measured between items do not consider time factors such as the sequence of rating, which could introduce biases caused by contrast effects. In this research, we proposed a new approach that combines both structural and rating-based similarity measurement used in memory-based CFs. We found that memory-based CF using combined similarity measurement can achieve better prediction accuracy than model-based CFs in terms of lower MAE and reduce memory and time by using less neighbors than traditional memory-based CFs on MovieLens and Netflix datasets. We also proposed techniques to reduce the biases caused by those user comparing, anchoring and adjustment behaviors by introducing the time-aware similarity measurements used in memory-based CFs. At last, we introduced novel techniques to identify, quantify, and visualize user preference dynamics and how it could be used in generating dynamic recommendation lists that fits each user’s current preferences.</p></div></div></div>

Recommender System is a tool helping users find content and overcome information overload. It predicts interests of users and makes recommendation according to the interest model of users. The original content-based recommender system is the continuation and development of collaborative filtering, which doesn’t need the user’s evaluation for items. Instead, the similarity is calculated based on the information of items that are chose by users, and then make the recommendation accordingly. With the improvement of machine learning, current content-based recommender system can build profile for users and products respectively. Building or updating the profile according to the analysis of items that are bought or visited by users. The system can compare the user and the profile of items and then recommend the most similar products. So this recommender method that compare user and product directly cannot be brought into collaborative filtering model. The foundation of content-based algorithm is acquisition and quantitative analysis of the content. As the research of acquisition and filtering of text information are mature, many current content-based recommender systems make recommendation according to the analysis of text information. This paper introduces content-based recommender system for the movie website of VionLabs. There are a lot of features extracted from the movie, they are diversity and unique, which is also the difference from other recommender systems. We use these features to construct movie model and calculate similarity. We introduce a new approach for setting weight of features, which improves the representative of movies. Finally we evaluate the approach to illustrate the improvement. / Recommender System är ett verktyg som hjälper användarna att hitta innehåll och övervinna informationsöverflöd. Det förutspår användarnas intressen och gör rekommendation enligt räntemodellen användare. Den ursprungliga innehållsbaserade recommender är en fortsättning och utveckling av samarbete filtrering, som inte behöver användarens utvärdering artiklar. Istället är likheten beräknas baserat på informationen objekt som har varit valde av användare, och sedan göra rekommendationen därefter. Med förbättringen av maskininlärning, kan nuvarande innehållsbaserad recommender systemet bygga profil för användare och produkt respektive. Bygga eller uppdatera profilen enligt analysen av objekt som köps eller besöks av användare. Systemet kan jämföra användaren och profilen av artiklar och rekommendera den mest liknande produkt. Så här recommender metod som jämför användaren och produkten direkt kan inte föras in collaborative filtreringsmodell. Grunden för innehållsbaserad algoritm är förvärv och kvantitativ analys av innehållet. Eftersom forskning förvärv och filtrering av textinformation är mogen, många aktuella innehållsbaserade recommender system gör rekommendation enligt analysen av textinformation. Denna uppsats införa innehållsbaserad recommender system för film webbplats VionLabs. Det finns en mängd funktioner som extraherats från en film, är de mångfald och unik, vilket är också skillnaden med andra recommender system. Vi använder dessa funktioner för att konstruera film vektor och beräkna likheter. Vi introducerar en ny metod för att fastställa vikten av funktioner, vilket förbättrar företrädare för filmer. Slutligen utvärderar vi tillvägagångssättet för att illustrera förbättringen.

A recommender system is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. These systems are present in a wide variety of applications and websites today. We can be aware of these recommendations when we are buying and articles similar to those we are looking for are suggested to us. However, they act in many other activities, such as in applications about restaurants and vacation trips. They also filter information from multimedia collections, such as Netflix or Amazon Prime. And furthermore, they are also present in browsers and they filter papers and books from repositories. They are subject to continuous research and improvement and the study of how these systems are being examined and evolve today is important because they literally filter the available information for us. This bibliometric study analyses the present-day research front on recommender systems. The chosen data source is the Web of Science bibliographic database and the study is performed following quantitative methods, using bibliometric techniques together with a qualitative assessment and interpretation of the most relevant research articles.

With an overwhelming number of movies available globally, it can be a daunting task for users to find movies that cater to their individual preferences. The vast selection can often leave people feeling overwhelmed, making it challenging to pick a suitable movie. As a result, movie service providers need to offer a recommendation system that adds value to their customers. A movie recommendation system can help customers in this regard by providing a process that assists in finding movies that match their preferences. Previous studies on recommendation systems that use Machine Learning (ML) algorithms have demonstrated that these algorithms outperform some of the existing recommendation methods regarding recommendation strategy. However, there is still room for further improvement, especially when it comes to exploring scenarios where users need to spend a considerable amount of time finding movies related to their preferred genres. This prolonged search for the right movies can give rise to problems such as data sparsity and cold start. To address these issues, we propose a machine learning-based recommender system for movie genres using the K-nearest Neighbours (KNN) algorithm. Our final system utilizes a slider bar on a Streamlit web app, allowing users to select their preferred movies and see recommendations for similar movies. By incorporating user preferences, our system provides personalized recommendations that are more likely to meet the user's interests and preferences. To address our research question: “How and to what extent can a machine learning-based recommender system be developed focusing on movie genres where movie popularity can be predicted based on its content?” we propose three main research objectives. Firstly, we investigate the employment of a classification algorithm in recommending movies focusing on interest genres. Secondly, we evaluate the performance of our classification algorithm concerning movie viewers. Thirdly, we represent the popularity of movie genres based on the content and investigate how this representation can inform the movie recommendation algorithm. On the heels of an experimental strategy, we extract and pre-process a dataset of movies and their associated genre labels from Kaggle. The dataset consists of two files derived from The Movie Database (TMDB) 5000 Movie Dataset. We develop a machine learning-based recommender system based on the similarity of movie genres using the extracted and pre-processed dataset. We vary the KNN algorithm with a slider bar to recommend movies of varying similarity to the selected movie, ranging from similar to diverse in genre. This approach can suggest movies with different titles for users with diverse preferences. We evaluate the performance of the KNN classification algorithm using a user's interest genres, measuring its accuracy, precision, recall, and F1-score. The algorithm's accuracy ranges from low to moderate across different values of K, indicating its moderate effectiveness in predicting user preferences. The algorithm's precision ranges from moderate to high, implying that it provides accurate recommendations to the user. The recall score improves with increasing K and reaches its maximum at K=15, demonstrating its ability to retrieve relevant recommendations. The algorithm achieves a good balance between precision and recall, with an average F1-score of 0.60. This means that the algorithm can accurately identify relevant movies and recommend them to users with a high degree of accuracy. Furthermore, our result shows that the popularity visualization technique using KNN is a powerful tool for analysing and understanding the popularity of different movie genres, which can inform important decisions related to marketing, distribution, and production in the movie industry. In conclusion, our machine learning-based recommender system using KNN for movie genres is a game changer. It allows users to select their preferred movies and see recommendations for similar movies using a slider bar on a Streamlit web app. If confirmed by future research, the promising findings of this thesis can pave the way for developing and incorporating other classification algorithms and features for movie recommendation and evaluation. Furthermore, the adjustable slider bar ranges on the Streamlit web app allow users to customize their movie preferences and receive tailored recommendations.

The amount of video content online will nearly triple in quantity by 2021 compared to 2016. The implementation of sophisticated filters is of paramount importance to manage this information flow. The research question of this thesis asks to what extent it is possible to generate personal recommendations, based on the data that news videos implies. The objective is to evaluate how different recommender systems compare to complete random, each other and how they are received by users in a test environment. This study was performed during the spring of 2018, and explore four different algorithms. These recommender systems include a content-based, a collaborative-filter, a hybrid model and a popularity model as a baseline. The dataset originates from a news media startup called Newstag, who provide video news on a global scale. The data is sparse and includes implicit feedback only. Three offline experiments and a user test were performed. The metric that guided the algorithms offline performance was their recall at 5 and 10, due to the fact that the top list of recommended items are of most interest. A comparison was done on different amounts of meta-data included during training. Another test explored respective algorithms performance as the density of the data increased. In the user test, a mean opinion score was calculated based on the quality of recommendations that each of the algorithms generated for the test subjects. The user test also included randomly sampled news videos to compare with as a baseline. The results indicate that for this specific setting and data set, the content-based recommender system performed best in both the recall at five and ten, as well as in the user test. All of the algorithms outperformed the random baseline. / Mängden video som finns tillgänglig på internet förväntas att tredubblas år 2021 jämfört med 2016. Detta innebär ett behov av sofistikerade filter för att kunna hantera detta informationsflöde. Detta examensarbete ämnar att svara på till vilken grad det går att generera personliga rekommendationer baserat på det data som nyhetsvideo innebär. Syftet är att utvärdera och jämföra olika rekommendationssystem och hur de står sig i ett användartest. Studien utfördes under våren 2018 och utvärderar fyra olika algoritmer. Dessa olika rekommendationssystem innefattar tekniker som content-based, collaborative-filter, hybrid och en popularitetsmodell används som basvärde. Det dataset som används är glest och har endast implicita attribut. Tre experiment utförs samt ett användartest. Mätpunkten för algoritmernas prestanda utgjordes av recall at 5 och recall at 10, dvs. att man mäter hur väl algoritmerna lyckas generera värdefulla rekommendationer i en topp-fem respektive topp-10-lista av videoklipp. Detta då det är av intresse att ha de mest relevanta videorna högst upp i sin lista av resultat. En jämförelse gjordes mellan olika mängd metadata som inkluderades vid träning. Ett annat test gick ut på att utforska hur algoritmerna presterar då datasetet blir mindre glest. I användartestet användes en utvärderingsmetod kallad mean-opinion-score och denna räknades ut per algoritm genom att testanvändare gav betyg på respektive rekommendation, baserat på hur intressant videon var för dem. Användartestet inkluderade även slumpmässigt generade videos för att kunna jämföras i form av basvärde. Resultaten indikerar, för detta dataset, att algoritmen content-based presterar bäst både med hänsyn till recall at 5 &amp; 10 samt den totala poängen i användartestet. Alla algoritmer presterade bättre än slumpen.