  Recommending Chemical Compounds of interest to a particular researcher is a
poorly explored field. The few existent datasets with information about the
preferences of the researchers use implicit feedback. The lack of Recommender
Systems in this particular field presents a challenge for the development of
new recommendations models. In this work, we 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). We 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.
