Armendáriz Íñigo, José Enrique
Loading...
Email Address
person.page.identifierURI
Birth Date
Job Title
Last Name
Armendáriz Íñigo
First Name
José Enrique
person.page.departamento
Estadística, Informática y Matemáticas
person.page.instituteName
ISC. Institute of Smart Cities
ORCID
person.page.observainves
person.page.upna
Name
- Publications
- item.page.relationships.isAdvisorOfPublication
- item.page.relationships.isAdvisorTFEOfPublication
- item.page.relationships.isAuthorMDOfPublication
2 results
Search Results
Now showing 1 - 2 of 2
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 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.