Armendáriz Íñigo, José Enrique
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Armendáriz Íñigo
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José Enrique
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Estadística, Informática y Matemáticas
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ISC. Institute of Smart Cities
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Publication Embargo When reviews speak through pictures: visual content and its influence on helpfulness(Elsevier, 2025-10-01) Vidaurreta Apesteguía, Paula; Alzate Barricarte, Miriam; Arce Urriza, Marta; Armendáriz Íñigo, José Enrique; D'Acunto, David; Gestión de Empresas; Enpresen Kudeaketa; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Research in Business and Economics - INARBE; Institute of Smart Cities - ISCThis research investigates the impact of service quality dimensions displayed in user-generated photos on their perceived helpfulness. Building on the SERVQUAL model and the Haywood-Farmer framework, we propose a novel methodology that integrates advanced image-to-caption techniques with topic modeling algorithms and negative binomial regression to extract, interpret, and quantify the effect of visuals on review helpfulness. Two studies were conducted relying on two sample of online reviews from two tourism-related service types (5,293 hotel reviews from Cancun, Mexico, and 11,252 spa and wellness reviews from Iceland). The results underline the role of visuals in affecting review helpfulness, with aspects such as “Room” “Leisure” and “Hotel Outdoor” positively impacting review helpfulness in hotels and “Natural Water Features” emerging as significant in spa and wellness reviews. Overall, this study underscores the relevance of tangibles and empathy in service evaluation, providing actionable strategies for businesses to optimize visual content.Publication Open 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 IngeniaritzaMany 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.Publication Open 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 KudeaketaRecommendation 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.