The paper “Hybrid Semantic Recommender System for Chemical Compounds”, co-authored by Márcia Barros, Francisco M. Couto (LASIGE researchers) and André Moitinho has been accepted to ECIR 2020, a top-ranked networking conference (CORE A) that will be held in Lisbon in April 2020.

This work propose a Hybrid recommender model for recommending Chemical Compounds. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking(BPR)) and semantic similarity between the Chemical Compounds in the ChEBI ontology (ONTO). The researchers evaluated the model in an implicit dataset of Chemical Compounds, CheRM. The Hybrid model was able to improve the results of state-of-the-art collaborative-filtering algorithms, especially for Mean Reciprocal Rank, with an increase of 6.7% when comparing the collaborative-filtering ALS and the Hybrid ALS_ONTO.