16 citations · 24 across the 4 of their papers we have counts for
5 papers
Behavior-Mining, Generative Conversations, and Collaborative Advisory: the Future of Travel and Tourism Recommender Systems
Alejandro Bellogín, Linus W. Dietz, Francesco Ricci +1
Since the early adoption of e-commerce, travel and tourism has been a lab for the design of recommender systems: tools that help travelers choose destinations, flights, accommodati…
Point of Interest Recommendation: Pitfalls and Viable Solutions
Alejandro Bellogín, Linus W. Dietz, Francesco Ricci +1
Point of interest (POI) recommendation can play a pivotal role in enriching tourists' experiences by suggesting context-dependent and preference-matching locations and activities,…
Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study
Robin Ungruh, Alejandro Bellogín, Dominik Kowald +1
Children are often exposed to items curated by recommendation algorithms. Yet, research seldom considers children as a user group, and when it does, it is anchored on datasets wher…
Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms
Linus W. Dietz, Pablo Sánchez, Alejandro Bellogín
Point-of-interest (POI) recommendations are essential for travelers and the e-tourism business. They assist in decision-making regarding what venues to visit and where to dine and…
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
Vito Walter Anelli, Daniele Malitesta, Claudio Pomo +3
The success of graph neural network-based models (GNNs) has significantly advanced recommender systems by effectively modeling users and items as a bipartite, undirected graph. How…