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Tamim Mahmud Al-Hasan, Aya Nabil Sayed, Faycal Bensaali, Yassine Himeur, Iraklis Varlamis and George Dimitrakopoulos
Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these appr...
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Abdelghani Azri, Adil Haddi and Hakim Allali
Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user?item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in ...
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Christos Troussas, Akrivi Krouska, Antonios Koliarakis and Cleo Sgouropoulou
Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. ?his paper presents a novel approach to providing custom...
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Georgios Chalkiadakis, Ioannis Ziogas, Michail Koutsmanis, Errikos Streviniotis, Costas Panagiotakis and Harris Papadakis
In this paper, we develop a novel hybrid recommender system for the tourism domain, which combines (a) a Bayesian preferences elicitation component which operates by asking the user to rate generic images (corresponding to generic types of POIs) in order...
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Michail Salampasis, Alkiviadis Katsalis, Theodosios Siomos, Marina Delianidi, Dimitrios Tektonidis, Konstantinos Christantonis, Pantelis Kaplanoglou, Ifigeneia Karaveli, Chrysostomos Bourlis and Konstantinos Diamantaras
Research into session-based recommendation systems (SBSR) has attracted a lot of attention, but each study focuses on a specific class of methods. This work examines and evaluates a large range of methods, from simpler statistical co-occurrence methods t...
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Sumet Darapisut, Komate Amphawan, Nutthanon Leelathakul and Sunisa Rimcharoen
Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user?s geographic location and contextual factors such as time, personal preference, and location categories. However, ...
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Alireza Javadian Sabet, Mahsa Shekari, Chaofeng Guan, Matteo Rossi, Fabio Schreiber and Letizia Tanca
One of the travelers? main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve ...
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Laila Esheiba, Amal Elgammal, Iman M. A. Helal and Mohamed E. El-Sharkawi
Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of indiv...
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Markos Konstantakis, Georgios Alexandridis and George Caridakis
Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, r...
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Aldo Gordillo, Daniel López-Fernández and Katrien Verbert
Open educational resources (OER) can contribute to democratize education by providing effective learning experiences with lower costs. Nevertheless, the massive amount of resources currently available in OER repositories makes it difficult for teachers a...
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