A Group Recommender System for Article Recommendation Using Matrix Factorization

These days, the Internet contains an overwhelming amount of data for users looking for specific information. This is why we use recommender systems to deal with information overload problems. Among several issues, this research focuses primarily on recommending articles to users who belong to a group. The groups are pre-defined based on employees’ work roles and can be further divided into sub-groups. We use different group recommendation and sub-grouping techniques to decide which one gives optimal results. Three recommendation techniques have been applied to suggest articles to the groups, namely: Before Factorization, After Factorization, and Weighted Before Factorization. In the experiment, Weighted Before Factorization achieves the best results on our dataset, collected from a company’s internal content management system. We have also proposed an enhancement to the above group recommendation models using clustering methods to create further subgroups. Compared with the results on the original pre-defined groups, k-means sub-grouping improves the F1@5, F1@10, F1@15 by 35.75%, 19.52% and 1.54% respectively.