Solving the cold-start problem in recommender systems with social tags

Based on the user-tag-object tripartite graphs, we propose a recommendation algorithm that makes use of social tags. Besides its low cost of computational time, the experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can enhance the algorithmic accuracy and diversity. Especially, it provides more personalized recommendation when the assigned tags belong to more diverse topics. The proposed algorithm is particularly effective for small-degree objects, which reminds us of the well-known cold-start problem in recommender systems. Further empirical study shows that the proposed algorithm can significantly solve this problem in social tagging systems with heterogeneous object degree.