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Hybrid Semantic Recommender System for Chemical Compounds
arXiv - CS - Information Retrieval Pub Date : 2020-01-21 , DOI: arxiv-2001.07440
Marcia Barros, Andr\'e Moitinho, Francisco M. Couto

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.

中文翻译:

化合物的混合语义推荐系统

向特定研究人员推荐感兴趣的化合物是一个探索不足的领域。少数现有的包含研究人员偏好信息的数据集使用隐式反馈。在这个特定领域缺乏推荐系统对新推荐模型的开发提出了挑战。在这项工作中,我们提出了一种用于推荐化合物的混合推荐模型。该模型集成了用于隐式反馈的协同过滤算法(交替最小二乘法 (ALS) 和贝叶斯个性化排名 (BPR))以及 ChEBI 本体 (ONTO) 中化合物之间的语义相似性。我们在化学化合物的隐式数据集 CheRM 中评估了模型。混合模型能够改进最先进的协同过滤算法的结果,
更新日期:2020-01-22
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