Recommending human resources to project leaders using a collaborative filtering-based recommender system: Case study of gitHub

摘要

Recommender systems (RSs) are a significant subclass of the information filtering system. RSs seek to predict the rating or preference that a user would give to an item in various online application community fields. Collaborative filtering (CF) is a technique which predicts user distinctions by learning past user-item relationships. However, it is hard to perceive the comparable interests between customers in light of the fact that the sparsity problem is caused by the deficient number of the relationship between users. It is a challenge which limited the ease of use of CF. This paper proposes a novel fuzzy C-means clustering approach which is used to deal with this sparsity problem by utilising a sparsest sub-graph detection algorithm in defining initial centres of the clustering method. The approach uses adaptability of fuzzy logic to make better personalised recommendations in terms of precision, recall and F-measure. The authors present a case study where GitHub is used to show the effectiveness of authors' approach. Authors' model can recommend relevant human resources (HR) to project leaders who have participated in similar projects. The comparative experiment results show that the planned approach will effectively solve the sparseness drawback and produce suitable coverage rate and recommendation quality.