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Armendáriz Íñigo, José Enrique

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Armendáriz Íñigo

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José Enrique

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Ingeniería Matemática e Informática

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0000-0002-2666-7191

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2530

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2016 - 20203
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    PublicationOpen Access
    Dobles titulaciones internacionales
    (2016) Armendáriz Íñigo, José Enrique; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    Presentación de las dobles titulaciones dentro de los proyectos Erasmus+: objetivos; requisitos para construir una doble titulación; consideraciones; ventajas para los estudiantes.
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    PublicationOpen Access
    Scalability approaches for causal multicast: a survey
    (Springer, 2016) Juan Marín, Rubén de; Decker, Hendrik; Armendáriz Íñigo, José Enrique; Bernabéu Aubán, José M.; Muñoz Escoí, Francesc D.; Ingeniería Matemática e Informática; Matematika eta Informatika Ingeniaritza
    Many distributed services need to be scalable: internet search, electronic commerce, e-government... In order to achieve scalability those applications rely on replicated components. Because of the dynamics of growth and volatility of customer markets, applications need to be hosted by adaptive systems. In particular, the scalability of the reliable multicast mechanisms used for supporting the consistency of replicas is of crucial importance. Reliable multicast may propagate updates in a predefined order (e.g., FIFO, total or causal). Since total order needs more communication rounds than causal order, the latter appears to be the preferable candidate for achieving multicast scalability, although the consistency guarantees based on causal order are weaker than those of total order. This paper provides a historical survey of different scalability approaches for reliable causal multicast protocols.
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    PublicationOpen Access
    BRScS: a hybrid recommendation model fusing multi-source heterogeneous data
    (SpringerOpen, 2020) Ji, Zhenyan; Yang, Chun; Wang, Huihui; Armendáriz Íñigo, José Enrique; Arce Urriza, Marta; Estadística, Informática y Matemáticas; Gestión de Empresas; Estatistika, Informatika eta Matematika; Enpresen Kudeaketa
    Recommendation systems are often used to solve the problem of information overload on the Internet. Many types of data can be used for recommendations, and fusing different types of data can make recommendations more accurate. Most existing fusion recommendation models simply combine the recommendation results from different data instead of fully fusing multi-source heterogeneous data to make recommendations. Furthermore, users’ choices are usually affected by their direct and even indirect friends’ preferences. This paper proposes a hybrid recommendation model BRScS (an acronym for BPR-Review-Score-Social). It fully fuses social data, score, and review together; uses improved BPR model to optimize the ranking; and trains them in a joint representation learning framework to get the top-N recommendations. User trust model is used to introduce social relationships into the rating and review data, PV-DBOW model is used to process the review data, and fully connected neural network is used to process the rating data. Experiments on Yelp public dataset show that the BRScS algorithm proposed outperforms other recommendation algorithms such as BRSc, UserCF, and HRSc. The BRScS model is also scalable and can fuse new types of data easily.