Abstract: Socialized recommendation has become one of the hot research topics in recent years. Introducing users’ social relationships into the recommendation algorithm based on their historical behavior can alleviate the problems of data sparsity and cold start faced by recommendation systems. This paper proposes a relative trust enhancement recommendation algorithm based on the CosRA (RTECosRA). In the bipartite “user-object” network, this algorithm allocates resources based on the CosRA similarity index and introduces users’ trust relationships during the resource allocation process. It adjusts the resource values obtained by trusted users, thereby increasing the recommendation rate of the items selected by trusted users. The results on the FriendFeed and Epinions datasets show that compared with the baseline algorithms, the RTECosRA algorithm has improvements in both accuracy and diversity. Moreover, by incorporating trust relationships, it expands the recommendable range for users and alleviates the cold start problem to a certain extent.

Key words: social networks, recommender algorithm, trust relationships, cold start