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Web Application of Recommender System
Koníček, Igor ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This master's thesis describes creation of recommender system that is used in real server cbdb.cz. A~fully operational recommender system was developed using collaborative and content-based filtering techniques. Thanks to many user feedback, we were able to evaluate their opinion. Many recommended books were tagged as desirable. This thesis is extending current functionality of cbdb.cz with recommender system. This system uses its extensive database of ratings, users and books.
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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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Music recommendation based on music information retrieval
Semela, René ; Schimmel, Jiří (referee) ; Kiska, Tomáš (advisor)
This thesis deals with the design, implementation and testing of the content-based music recommender system based on music information retrieval. In the introduction the attention is paid to issues of music information retrieval and to areas of their utilization, it also focuses on tools of their retrieving. Aferwards the most used types of recommender systems are described, including their typical problems. Options of the hybridization of these systems as well as examples of the popular music recommender systems are mentioned. There also is an outline of their functioning. The following section is focused on the parameterization of musical pieces and is devoted to the description of particular most used parameters. The next section is devoted to the content-based music recommender system design itself, including the defining of particular parameters that are used to differentiate musical recordings using the algorithm mRMR and other procedures. The recommender system design as such is oriented to the classification method k-nearest neighbors. The attention is also paid to the model of user taste recorded by Rocchio algorithm. In the next section the system is implemented according to the design. There is also described its functionality including the background processes. The final part of this work is focused on system testing and evaluation.
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Web Application of Recommender System
Hlaváček, Pavel ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with problems of recommender systems and their usage in web applications. There are three main data mining techniques summarized and individual approaches for recommendation. Main part of this thesis is a suggestion and an implementation of web applications for recommending dishes from restaurants. Algorithm for food recommending is designed and implemented in this paper. The algorithm deals with the problem of frequently changing items. The algorithm utilizes hybrid filtering technique which is based on content and knowledge. This filtering technique uses cosine vector similarity for computation.
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Simple Recommender System
Gorčák, Damián ; Rychlý, Marek (referee) ; Bartík, Vladimír (advisor)
Recommender systems are very important in searching for items all over the internet. There are many algorithms for creating recommendations. The main goal of this thesis was to find suitable datasets and make application, which would process them. After that, chosen algorithms for recommender systems are compared with selected datasets
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Recommender System for Web Articles
Kočí, Jan ; Kesiraju, Santosh (referee) ; Fajčík, Martin (advisor)
Tématem této bakalářské práce jsou doporučovací systémy pro webové články. Tato práce nejdříve uvádí nejpopulárnější metody z této oblasti a vysvětluje jejich principy, následně navrhuje požití vlastní architektury, založené na neuronových sítích, která aplikuje metodu Skip-gram negative sampling na problematiku doporučování. V další části pak implementuje tuto architekturu společně s několika dalšími modely, požívající algoritmus SVD, collaborative filtering s algoritmem ALS a také metodu Doc2Vec k vytvoření vektorové reprezentace z obsahu získaných článků. Na závěr vytváří tři evaluační metriky, konkrétně metriky RANK, Recall at k a Precision at k, a vyhodnocuje kvalitu implementovaných modelů srovnáním výsledků s nejmodernějšími modely. Kromě toho také diskutuje o roli a smyslu doporučovacích systémů ve společnosti a uvádí motivaci pro jejich používání.
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Educational System for Recommending Study Activities
Zapletal, Jakub ; Bartík, Vladimír (referee) ; Burget, Radek (advisor)
Cílem této práce je navrhnout a implementovat modul do existujícího doporučovacího systému Open University v Milton Keynes. Nyní nasazený doporučovací systém využívá informací o aktivitě uživatelů ve Virtual Learning Environment (VLE) nasbíraných z předchozích let a podle ní doporučuje studentům relevantní studijní aktivity. Tento modul využívá sémantické podobnosti mezi studijními materiály k doporučení těch, které pomohou uživateli vyřešit úkol nebo které jsou podobné k těm, o něž projevil zájem.K počítání podobnosti dokumentů je využíváno metod Term Frequency - Inverse Document Frequency a vnoření slov.Pro používání modulu a jeho komunikaci modulu s rozhraním OU Analyse je implementováno RESTful API.
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Simple Recommender System
Gorčák, Damián ; Rychlý, Marek (referee) ; Bartík, Vladimír (advisor)
Recommender systems are very important in searching for items all over the internet. There are many algorithms for creating recommendations. The main goal of this thesis was to find suitable datasets and make application, which would process them. After that, chosen algorithms for recommender systems are compared with selected datasets
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Recommender systems - models, methods, experiments
Peška, Ladislav ; Vojtáš, Peter (advisor) ; Jannach, Dietmar (referee) ; Krátký, Michal (referee)
This thesis investigates the area of preference learning and recommender systems. We concentrated recommending on small e-commerce vendors and efficient usage of implicit feedback. In contrast to the most published studies, we focused on investigating multiple diverse implicit indicators of user preference and substantial part of the thesis aims on defining implicit feedback, models of its combination and aggregation and also algorithms employing them in preference learning and recommending tasks. Furthermore, a part of the thesis focuses on other challenges of deploying recommender systems on small e-commerce vendors such as which recommending algorithms should be used or how to employ third party data in order to improve recommendations. The proposed models, methods and algorithms were evaluated in both off-line and on-line experiments on real world datasets and on real e-commerce vendors respectively. Datasets are included to the thesis for the sake of validation and further research. Powered by TCPDF (www.tcpdf.org)
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