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Content based Recommendation from Explicit Ratings
Ferenc, Matej ; Vojtáš, Peter (advisor) ; Peška, Ladislav (referee)
In the thesis we compare several models for prediction of user preferences. The focus is mainly on Content Based models which work with metadata about objects that are recommended. These models are compared with other models which do not use metadata for recommendation. We use three datasets and three metrics to get the results of recommendation. The goal of the thesis is to find out how can the metadata about the users and the objects enhance the standard recommender models. However, the result is that the metadata can enhance recommendation in some cases, but it varies by used metrics and dataset. This enhancement is not significant.
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User preferences in the domain of web shops
Peška, Ladislav ; Vojtáš, Peter (advisor) ; Eckhardt, Alan (referee)
The goal of the thesis is first to find available information about user preferences, user feedback and their acquisition, processing, storing etc. The collected information is then used for making suggestions / advices for the creating an recommender system for the web shops (with special emphasis on implicit feedback). The following chapters introduces UPComp - our solution of the recommender system for the web shops. The UPComp is written in the programming language PHP and uses MySQL database. The thesis also includes testing of the UPComp on real-user web shop sites slantour.cz and antikvariat-ichtys.cz.
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Univerzální doporučovací systém
Cvengroš, Petr ; Vojtáš, Peter (advisor) ; Dědek, Jan (referee)
Recommender systems are programs that aim to present items like songs or books that are likely to be interesting for a user. These systems have become increasingly popular and are intensively studied by research groups all over the world. In web systems, like e-shops or community servers there are usually multiple data sources we can use for recommending, as user and item attributes, user-item rating or implicit feedback from user behaviour. In the thesis, we present a concept of a Universal Recommender System (Unresyst) that can use these data sources and is domain-independent at the same time. We propose how Unresyst can be used. From the contemporary methods of recommending, we choose a knowledge based algorithm combined with collaborative filtering as the most appropriate algorithm for Unresyst. We analyze data sources in various systems and generalize them to be domain-independent. We design the architecture of Unresyst, describe its interfaces and methods for processing the data sources. We adapt Unresyst to three real-world data sets, evaluate the recommendation accuracy results and compare them to a contemporary collaborative filtering recommender. The comparison shows that combining multiple data sources can improve the accuracy of collaborative filtering algorithms and can be used in systems where...
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Using customer preferences in property market
Strnad, Radek ; Kopecký, Michal (advisor) ; Peška, Ladislav (referee)
In recent years the market share of major real estate companies, at least the Czech ones, has not changed much. Statistical data don't reflect any significal upward trend in volumes of properties for rent or sale. In case the real estate company would like to access larger market share, they have to secure a competitive advantage over the others. One of the ways how to attract more potential customers might be speeding up the company website's property search process. In many cases the website visitors are facing hundreds or thousands of property offers before finding couple satisfactories. The aim of the thesis is to explore possibilities of applicating customer preferences in property trading. The focus is put on research of recommender system algorithms, their characteristics and limtations. The author is evaluating usage of each algorithm variant and its suitability for a real world deployment in a real estate area. Apart from the theoretical part of the work one can find a part, where real estate information system is extended with a framework for implementing recommendation system algorithms. The author is in possesion of production data of a medium sized real estate company. He uses the recommender system framework to build and evaluate example algorithm. Powered by TCPDF (www.tcpdf.org)
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Deep Learning For Implicit Feedback-based Recommender Systems
Yöş, Kaan ; Peška, Ladislav (advisor) ; Balcar, Štěpán (referee)
The research aims to focus on Recurrent Neural Networks (RNN) and its application to the session-aware recommendations empowered by implicit user feedback and content-based metadata. To investigate the promising architecture of RNN, we implement seven different models utilizing various types of implicit feedback and content information. Our results showed that using RNN with complex implicit feedback increases the next-item prediction comparing the baseline models like Cosine Similarity, Doc2Vec, and Item2Vec.
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Recommender systems for fashion outfits
Nepožitek, David ; Peška, Ladislav (advisor) ; Skopal, Tomáš (referee)
Outfit recommendation is a task of suggesting fashion products that complement a given set of garments. Traditional recommender systems rely primarily on similarities between items or users; however, that is not sufficient for a recommendation of comple- mentary products. Thus, outfit recommendation systems use machine learning techniques to learn more subtle relations between items. In this thesis, we explore the possibility of employing recent natural language processing approaches in outfit recommendation. We propose a novel architecture based on the Transformer, and we evaluate the model on standard datasets. We show that our approach is capable of learning some relations between items. However, its performance does not exceed the state-of-the-art models. 1
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Movie Recommender System
Janko, Pavel ; Zbořil, František (referee) ; Šůstek, Martin (advisor)
This thesis primarily addresses various methods of constructing a system for movie recommendations. Both basic and advanced techniques required for creating a recommender system are also covered in the thesis. The core of the thesis is designing, implementing and experimenting with a system for movie recommendations based upon the data originating from publicly accessible datasets. In order to predict ratings that the user would give to movies after watching them, the system utilizes a factorization model based on collaborative filtering. This thesis also describes the relation between model hyperparameter configuration and prediction accuracy, experiments that were conducted in order to further improve the model accuracy and finally compares the implemented model with existing solutions.
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Film Suggestions Based on CSFD User Profiles
Janko, Pavel ; Šůstek, Martin (referee) ; Uhlíř, Václav (advisor)
This thesis covers the topic of utilizing neural nets for recommending movies. The principle of using neural nets with machine learning and both the general and the advanced techniques of creating a recommender system are also covered in the thesis. The core of the thesis is the design, implementation and finally the evaluation of a system for movie recommendations based upon the data mined from the user profiles from the ČSFD (Czech-Slovak film database). In order to accomplish this goal the system utilizies an explicit factorization model based on collaborative filtering between items to predict an accurate rating that the user would presumably give to a movie after watching it. This thesis also describes the relation between dataset size and prediction accuracy and demonstrates this accuracy by analyzing user feedback.
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Recommender systems for the recipes domain
Starýchfojtů, Josef ; Peška, Ladislav (advisor) ; Bernhauer, David (referee)
Recommender systems are now part of our daily life more than ever. We use them through several platforms, like music or video players. As users of such systems, we don't need to actively seek for new content, but let it be comfortably recommended to us instead. One area not covered this way is domain of recipes. In order to cook a recipe, we have to search for some cookbook and find the recipe there, then go shopping for it and only then cook it. Usually there is not a perfect match between the recipe content and the content of our shopping basket, which makes the process even more unpleasant. Main goal of this work is to introduce such platform into this domain. Firstly to research the application of recommender systems in recipe domain, suggestion of several methods and their verification. Then also to introduce these algorithms in real product in form of mobile application, which will accompany the user when shopping and choosing a recipe. 1
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