32 citations · 32 across the 6 of their papers we have counts for
7 papers
Oxytrees: Model Trees for Bipartite Learning
Pedro Ilídio, Felipe Kenji Nakano, Alireza Gharahighehi +3
Bipartite learning is a machine learning task that aims to predict interactions between pairs of instances. It has been applied to various domains, including drug-target interactio…
Pairwise and Attribute-Aware Decision Tree-Based Preference Elicitation for Cold-Start Recommendation
Alireza Gharahighehi, Felipe Kenji Nakano, Xuehua Yang +2
Recommender systems (RSs) are intelligent filtering methods that suggest items to users based on their inferred preferences, derived from their interaction history on the platform.…
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis
Alireza Gharahighehi, Achilleas Ghinis, Michela Venturini +2
Massive Open Online Courses (MOOCs) are emerging as a popular alternative to traditional education, offering learners the flexibility to access a wide range of courses from various…
HypeRS: Building a Hypergraph-driven ensemble Recommender System
Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos
Recommender systems are designed to predict user preferences over collections of items. These systems process users' previous interactions to decide which items should be ranked hi…
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering
Alireza Gharahighehi, Felipe Kenji Nakano, Celine Vens
Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to mod…
Diversification in Session-based News Recommender Systems
Alireza Gharahighehi, Celine Vens
Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites most of users are anonymous an…