In a knowledge‐intensive environment, a task in an organization is typically performed by a group of people who have task‐related knowledge and expertise. Each group may require task‐related knowledge of different topic domains and documents to accomplish its tasks. Document recommendation methods are very useful to resolve the information overload problem and proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. A worker's document referencing behavior can be modeled as a knowledge flow (KF) to represent the evolution of his information needs over time. However, the information needs of workers and groups may change over time, so that modeling the knowledge referencing behavior of a group of workers is difficult. Additionally, most traditional recommendation methods which provide personalized recommendations do not consider workers' KFs, or the information needs of the majority of workers in a group to recommend task knowledge. In this work, I integrate the KF mining method and propose group‐based recommendation methods, including group‐based collaborative filtering (GCF) and group content‐based filtering (GCBF), to actively provide task‐related documents for groups. Experimental results show that the proposed methods have better performance than the personalized recommendation methods in recommending the needed documents for groups. Thus, the recommended documents can fulfill the groups' task needs and facilitate knowledge sharing among groups.

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 behavior can be modeled as a knowledge flow (KF) to represent the evolution of his or 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’ KFs or the information needs of the majority of a group of workers with similar KFs. A group's needs may partially reflect the needs of an individual worker that cannot be inferred from his or her past referencing behavior. In other words, the group's knowledge complements that of the individual worker. Thus, we leverage the group perspective to complement the personal perspective by using hybrid approaches, which combine the KF‐based group recommendation method (KFGR) with traditional personalized‐recommendation methods. The proposed hybrid methods achieve a trade‐off between the group‐based and personalized methods by exploiting the strengths of both. The results of our experiment show that the proposed methods can enhance the quality of recommendations made by traditional methods.