Hybrid Semantic Recommender System for Chemical Compounds
This work addresses a poorly explored domain-specific problem for researchers in chemistry, but it is incremental as it builds on existing collaborative-filtering techniques.
The authors tackled the problem of recommending chemical compounds to researchers by proposing a hybrid model that combines collaborative-filtering algorithms with semantic similarity from an ontology, resulting in a 6.7% improvement in Mean Reciprocal Rank compared to a baseline method.
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.