Recommending documents via knowledge flow-based group recommendation

Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task-related codified knowledge (documents) to perform tasks. A worker's document referencing behaviour can be modelled as a knowledge flow (KF) to represent the evolution of his/her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers' knowledge flows and the information needs of the majority of a group of workers with similar knowledge flows. A group's needs may partially reflect the needs of an individual worker that cannot be inferred from his/her past referencing behaviour. Thus, we leverage the group perspective to complement the personal perspective by using a hybrid approach, which combines the KF-based group recommendation method (KFGR) with the user-based collaborative filtering method (UCF). The proposed hybrid method achieves a trade-off between the group-based and the personalized method by integrating the merits of both methods. Our experiment results show that the proposed method can enhance the quality of recommendations made by traditional methods.