论文标题
化合物的混合语义推荐系统
Hybrid Semantic Recommender System for Chemical Compounds
论文作者
论文摘要
推荐特定研究人员感兴趣的化合物是一个探索较差的领域。存在的少数数据集，其中包含有关研究人员偏好的信息使用隐式反馈。该特定领域缺乏推荐系统为开发新建议模型带来了挑战。在这项工作中，我们提出了一种用于推荐化合物的混合建议模型。该模型集成了用于隐式反馈的协作过滤算法（交替的最小二乘（ALS）和贝叶斯个性化排名（BPR））和Chebi本体学（to）中化合物之间的语义相似性。我们在化合物的隐式数据集中评估了该模型。混合模型能够改善最新的协作过滤算法的结果，尤其是对于平均互惠等级的结果，在比较协作过滤的ALS和混合ALS_ONTO时，增长了6.7％。
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.
