Searched for: subject%3A%22recommender%22
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Harte, Jesse (author)
In this thesis we aim to research and design different neural models for session recommendation. We investigate the fundamental neural models for session recommendation, namely BERT4Rec, SASRec and GRU4Rec and subsequently use our findings to design a simpler but performant neural model. <br/><br/>Firstly, we address methodological errors made...
master thesis 2023
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Li, Roger Zhe (author)
doctoral thesis 2023
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Lodha, Sneha (author)
<br/>One of the contributing factors to climate change is the release of gases, particularly carbon dioxide (CO2), which is amplified by the expanding E-commerce industry. E-commerce enterprises heavily depend on recommender systems as a means to incentivize consumers towards making product purchases. This master's thesis investigates the...
master thesis 2023
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Beyhan, Yessin (author), Pera, M.S. (author)
The aim of this work is to explore common traits preferred across different age groups of children to identify the appeal of book covers. By analyzing visual attributes, visible objects, and implied stories inferred from the covers, we can gain insights into the elements that are most attractive to children up to 18 years old. These findings...
conference paper 2023
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Harte, Jesse (author), Zorgdrager, Wouter (author), Louridas, Panos (author), Katsifodimos, A (author), Jannach, Dietmar (author), Fragkoulis, M. (author)
Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches. In this work, we explore how large language models (LLMs), which are nowadays introducing disruptive effects in many AI-based applications, can be used to build or...
conference paper 2023
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Maddila, C.S. (author)
The software development life cycle (SDLC) for a developer has increased in complexity and scale. With the advent of DevOps processes, the gap between development and operations teams reduced significantly. Developers are now expected to perform different roles from coding to operational support in the new model of software development. This...
doctoral thesis 2022
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Chandrashekar, Rohan (author)
Humans make decisions when presented with choices based on influences. The Internet today presents people with abundant choices to choose from. Recommending choices with an emphasis on people's preferences has become increasingly sought. Grundy (1979), the first computer librarian Recommender System (RS), provided users with book recommendations...
master thesis 2022
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Jongerius, Ricardo (author)
Online dating has become the most popular method of finding potential romantic partners. At the core of these platforms, there is a reciprocal recommender system which recommends users to other users on the platform. Breeze is an example of such a dating app, serving its users potential romantic partners every day in the hopes of sending them on...
master thesis 2022
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Kalisvaart, Raoul (author)
We all know the possible consequences of global warming, rising temperatures, flooded cities and destroyed ecosystems. One of the causes is the emission of gases, predominantly CO2, which is increased by the growing E-commerce market. E-commerce companies rely on recommender systems to stimulate users to purchase products. We are convinced that...
master thesis 2022
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Karahan, Asli (author)
Healthcare recommender systems emerged to help patients make better decisions for their health, leveraging the vast amount of data and patient experience. One type of this system focuses on recommending the most appropriate physician based on previous patient feedback in the form of ratings. Such advice can be challenging to generate for new...
master thesis 2022
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Pantea, Luca (author)
Recommender Systems play a significant part in filtering and efficiently prioritizing relevant information to alleviate the information overload problem and maximize user engagement. Traditional recommender systems employ a static approach towards learning the user's preferences, relying on logged previous interactions with the system,...
bachelor thesis 2022
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Mundhra, Yash (author)
Recommender systems are an essential part of online businesses in today's day and age. They provide users with meaningful recommendations for items and products. A frequently occurring problem in recommender systems is known as the long-tail problem. It refers to a situation in which a majority of the items in the data set have limited ratings...
bachelor thesis 2022
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Ionescu, Andrei (author)
Developers do not want to reinvent the wheel when developing software systems. Open-source software repositories are packed with resources that may assist developers with their work. Since Github enabled repository tagging, a new opportunity arose to help developers find the needed resources tailored to their needs. The current work proposes two...
bachelor thesis 2022
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Koper ook geschreven Jansen, Melle (author)
A recommendation algorithm aims to predict the quality of a user's future interaction with certain items based on their previous interactions. As research progresses, these algorithms are becoming increasingly more complicated with the use of machine learning and neural networks. This paper looks into a more simple solution. The recommendation...
bachelor thesis 2022
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Bobde, Sharwin (author)
Using Recommender Systems with Evolutionary Algorithms is an extremely niche domain. It holds the key to enabling new user interaction designs, where users can effectively configure their experience with a Recommender System. This thesis answers important questions about the scientific aspects of its application to large-scale data through a...
master thesis 2021
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Kim, Jaehun (author)
Machine learning (ML) has become a core technology for many real-world applications. Modern ML models are applied to unprecedentedly complex and difficult challenges, including very large and subjective problems. For instance, applications towards multimedia understanding have been advanced substantially. Here, it is already prevalent that...
doctoral thesis 2021
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Musto, Cataldo (author), Tintarev, N. (author), Inel, O. (author), Polignano, Marco (author), Semeraro, Giovanni (author), Ziegler, Jürgen (author)
Adaptive and personalized systems have become pervasive technologies that are gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interact every day with algorithms that help us in several scenarios, ranging from services that suggest us music to be listened to or movies to be watched, to personal...
conference paper 2021
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Mulder, Mats (author)
Previous research on diversity in recommender systems define diversity as the opposite of similarity and propose methods that are based on topic diversity. Diversity in news media, however, is understood as multiperspectivity and scholars generally agree that fostering diversity is the key responsibility of the press in a democratic society....
master thesis 2020
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Knyazev, Norman (author)
Many widely used Recommender System algorithms estimate user tastes without accounting for their evolving nature. In recent years there has been a gradual increase in methods incorporating such temporal dynamics through sequential processing of user consumption histories. Some works have also included additional temporal features such as time...
master thesis 2020
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Dingjan, Mitchell (author)
Recommender systems focus on automatically surfacing suitable items for users from digital collections that are too large for the user to oversee themselves. A considerable body of work exists on surfacing items that match what a user liked in the past; this way, the recommender system will exploit its knowledge of a user's comfort zone. However...
master thesis 2020
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