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Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Francisco J. Ribadas-Pena and Néstor Bolaños
In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are expe...
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Diego Sánchez-Moreno, Vivian F. López Batista, María Dolores Muñoz Vicente, Ángel Luis Sánchez Lázaro and María N. Moreno-García
Information from social networks is currently being widely used in many application domains, although in the music recommendation area, its use is less common because of the limited availability of social data. However, most streaming platforms allow for...
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Thi-Linh Ho, Anh-Cuong Le and Dinh-Hong Vu
Recommender systems are challenged with providing accurate recommendations that meet the diverse preferences of users. The main information sources for these systems are the utility matrix and textual sources, such as item descriptions, users? reviews, a...
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Antoine Falconnet, Constantinos K. Coursaris, Joerg Beringer, Wietske Van Osch, Sylvain Sénécal and Pierre-Majorique Léger
Advice-giving systems such as decision support systems and recommender systems (RS) utilize algorithms to provide users with decision support by generating ?advice? ranging from tailored alerts for situational exception events to product recommendations ...
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Chenhong Luo, Yong Wang, Bo Li, Hanyang Liu, Pengyu Wang and Leo Yu Zhang
Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However,...
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Dharahas Tallapally, John Wang, Katerina Potika and Magdalini Eirinaki
Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network dat...
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Rogério Xavier de Azambuja, A. Jorge Morais and Vítor Filipe
In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large ...
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Dimitris Koryzis, Dionisis Margaris, Costas Vassilakis, Konstantinos Kotis and Dimitris Spiliotopoulos
Exploitation and use of disruptive technologies, such as the Internet of Things, recommender systems, and artificial intelligence, with an ambidextrous balance, are a challenge, nowadays. Users of the technologies, and stakeholders, could be part of a ne...
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Chin-Yi Chen and Jih-Jeng Huang
Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking al...
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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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