||With the emergence of Internet, there is more and more information disseminating all over this channel. The abundant amount of information, however, causes difficulty for users to locate desired information, which is referred to as the information overload problem due to our limited processing ability. Therefore, recommender systems arise to assist users to acquire useful information based on their past preferences or collaborative preferences from other sources.|
Most of the previous research works focus on personalized recommendation for individuals. However, a more difficult issue is to make group recommendation. Group recommendation aims at recommending items for a group of users who participate in a social activity intentionally or randomly. It notably distinguishes itself from personalized recommendation in that collective group behavior needs to be addressed by taking individuals’ behaviors into account.
The objective of this research is thus to propose hybrid filtering approaches for group recommendation on documents. Particularly, latent Dirichlet allocation to uncover latent semantic structure in documents is incorporated to serve as a bridge to connect content-based filtering and collaborative filtering as a whole, and generate complementary and additive effects for better performance. Four experiments (two random group cases and two intentional group cases) are conducted accordingly. The results show that our proposed approaches (GCBPF and GSBCF) outperform other traditional group filtering approaches on the recommendation performance, which justifies the feasibility of our proposed approaches in applications.