Research on personalized recommendation algorithm based on trust relationship

Personalized recommendation, as an important technology to overcome information overload, has been widely used in various fields. However, due to the deficiencies and limitations of existing recommendation algorithms, traditional collaborative filtering recommendation algorithm does not consider the impact of multi-source social information, resulting in low recommendation accuracy. To solve the above problems, this paper proposes a personalized recommendation algorithm that integrates user trust relationship. Based on matrix decomposition, this algorithm fully considers the characteristics of explicit trust relationship, implicit trust relationship and trust propagation between users. The algorithm incorporates users' explicit trust relationships and implicit trust relationships through weight factors into the matrix factorization algorithm. The experimental results show that the accuracy of the proposed algorithm is improved significantly. © 2021, Politechnica University of Bucharest. All rights reserved.