Searched for: subject:"Recommender%5C+systems"
(1 - 19 of 19)
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Dritsas, Athanasios (author)
In the last years, the popularity of video-on-demand services has been constantly increasing, especially for the young audiences who are more adept at using new technologies. Through those platforms, the viewers have access to a huge volume of movies at any moment that makes the viewing decision for most of them a very challenging task....
master thesis 2019
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Jiang, Xuehan (author)
Information systems, such as information retrieval machines and recommendation systems, utilize various user information and history behaviors to provide personalized content to users. However, a debate on whether the personalization in information systems can trigger the online echo chamber effect has emerged. The online echo chamber effect...
master thesis 2018
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Pangaribuan, Andreas Christian (author)
Users may show a behavioral pattern in consuming the items. For example, one might assume that a user is interested in comedy movies when this user watches comedy movies frequently. Recommender systems are designed to understand the preference of a user from his interactions with the items and suggest items that correspond to his preference....
master thesis 2018
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Reza Aditya Permadi, Reza (author)
This thesis explores the effects of incorporating user consumption behavior and multiple types of user feedback to improve recommender systems for personalized music video television. An industrial use case is made possible by the availability of anonymized user interaction data on curation-based personalized music television system provided by...
master thesis 2018
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Ghanmode, Ishan (author)
In today’s digital world, users are often confronted with an abundance of information. Whether the user is looking to compare online prices for products, searching for new movies to watch or music to listen, the available information at hand exceeds the amount of information which the user wants to consider before making a choice. For this,...
master thesis 2018
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Kumar, Jaya (author)
In recent years, personalized recommender systems have been facing criticism in research due to their ability to trap users in their circle of choices, called "filter-bubble", thereby limiting their exposure to novel content. In solving the issue of filter-bubble, past research has focused on providing explanations to users about how a...
master thesis 2018
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Lu, Feng (author)
Current research on personality and diversity based Recommender Systems (RecSys) are mostly separated. In most diversity-based Recommender Systems, researchers usually endeavored to achieve an optimal balance between accuracy and diversity while they commonly set a same diversity level for all users. Different diversity needs for users with...
master thesis 2018
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van der Vlugt, Yanna (author)
Over the past three decades, the singular value decomposition has been increasingly used for various big data applications. As it allows for rank reduction of the input data matrix, it is not only able to compress the information contained, but can even reveal underlying patterns in the data through feature identification. This thesis explores...
bachelor thesis 2018
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Liang, Yu (author)
News recommendation is a field different from traditional recommendation fields. News articles are created and deleted continuously with a very short life cycle. Users' preference is also hard to model since they can easily be attracted by things happening around them. With all those challenges, traditional recommendation approaches, such as...
master thesis 2017
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Jiao, Chongze (author)
Recent years, recommender systems are more and more important for solving information overload problem. They sort through massive data to provide users with personalized content and services. Most researchers focus on designing new algorithms to increase the performance of recommender systems. However, some open challenges stand: Why the...
master thesis 2017
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van Kortenhof, B.L. (author)
Recommender systems help users explore a large data set by proposing items in that data set that the system expects to be of interest to that user. The use of context, information that describes in which situation a user interacts with the recommender system, has shown to increase the effectiveness of recommender systems in several domains. In...
master thesis 2017
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Arnason, B. (author)
The tremendous growth of the Internet brings with it a massive amount of data that users are exposed to on a daily basis. Consequently, information filtering techniques like recommender systems have become increasingly important to sift through the data and find what is relevant to a particular user. A recent approach for recommender systems,...
master thesis 2016
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Simes, A. (author)
When talking about personalization online, Google CEO Eric Schmidt recently said "it will be very hard for people to watch or consume something that has not in some sense been tailored for them." This level of personalized filtering of content has worried academics and activists. Many argue that users will be trapped in a so-called "Filter...
master thesis 2016
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Wafula, J.B. (author)
Many software systems are designed to be long-lived due to the costs involved in developing new systems. Changes in these systems are inevitable due to constant modifications in requirements that are necessitated by the constantly changing nature of the business environment or detection of faults.To adapt their software to all these changing...
master thesis 2015
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Chandrasekaran Ayyanathan, P.S.N. (author)
Since the advent of Web 2.0, users have not only increasingly created content, but also contributed reactions to content in the form of comments. Comments are challenging to analyze due to their short lengths and informal style, meaning that any individual comment provides very little data to work with and is highly variable. However, comments...
master thesis 2015
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Basak, D. (author)
Crowdsourcing and Human computation have enabled industry, and scientists to create innovative solutions by harnessing organized collective human effort. In human computation platforms, it is observed that workers spend a considerable amount of time searching for appropriate tasks, thus losing revenues that they could have made and, ultimately,...
master thesis 2014
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Basak, D. (author)
Crowdsourcing and Human computation have enabled industry, and scientists to create innovative solutions by harnessing organized collective human effort. In human computation platforms, it is observed that workers spend a considerable amount of time searching for appropriate tasks, thus losing revenues that they could have made and, ultimately,...
master thesis 2014
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Rentmeester, M. (author)
Most recommender systems recommend items from a single domain. However, usually users’ preferences span across multiple domains. Cross-domain recommender systems can successfully recommend items in multiple domains when there is knowledge about the user’s preferences for items in at least one of the domains and when there is knowledge about...
master thesis 2014
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Karczewski, M.P. (author)
The thesis presents the results of research into temporal preference analysis in recommender systems. Temporal preference analysis consists of methods for detecting time recurrent changes in user preferences and for using this information to improve the recommendation precision.
master thesis 2011
Searched for: subject:"Recommender%5C+systems"
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