Research of Group Recommendation Based on Matrix Factorization

In current big data era, it is more and more important to extract valuable data from big data, i.e. information filtering. Information filtering is categorized into personalized recommendation for the individual users and group recommendation; the later is the extension of the former and is more complex. The current group recommendation has quite a lot of shortages, such as ignoring the items' natural property, the users' interest and their behavior's temporal dynamics. Based on the advantages of matrix factorization on personalized recommendation, the paper adopts ridge regression analysis to propose a matrix factorization model (WMF model) weighted by the items; and combine the WMF model with time effect function to get further improvement. Experiments show that the proposed group recommendation has better precision and recall rate.