1 research outputs found
Leveraging Social Signal to Improve Item Recommendation for Matrix Factorization
Although Recommender Systems have been comprehensively studied in the past
decade both in industry and academia, most of current recommender systems
suffer from the following issues: 1) The data sparsity of the user-item matrix
seriously affect the recommender system quality. As a result, most of
traditional recommender system approaches are not able to deal with the users
who have rated few items, which is known as cold start problem in recommender
system. 2) Traditional recommender systems assume that users are independently
and identically distributed and ignore the social relation between users.
However, in real life scenario, due to the exponential growth of social
networking service, such as facebook and Twitter, social connections between
different users play an significant role for recommender system task. In this
work, aiming at providing a better recommender systems by incorporating user
social network information, we propose a matrix factorization framework with
user social connection constraints. Experimental results on the real-life
dataset shows that the proposed method performs significantly better than the
state-of-the-art approaches in terms of MAE and RMSE, especially for the cold
start users