  Matrix factorization (MS) is a collaborative filtering (CF) based approach,
which is widely used for recommendation systems (RS). In this research work, we
deal with the content recommendation problem for users in a content management
system (CMS) based on users' feedback data. The CMS is applied for publishing
and pushing curated content to the employees of a company or an organization.
Here, we have used the user's feedback data and content data to solve the
content recommendation problem. We prepare individual user profiles and then
generate recommendation results based on different categories, including Direct
Interaction, Social Share, and Reading Statistics, of user's feedback data.
Subsequently, we analyze the effect of the different categories on the
recommendation results. The results have shown that different categories of
feedback data have different impacts on recommendation accuracy. The best
performance achieves if we include all types of data for the recommendation
task. We also incorporate content similarity as a regularization term into an
MF model for designing a hybrid model. Experimental results have shown that the
proposed hybrid model demonstrates better performance compared with the
traditional MF-based models.
