A Recommendation Algorithm via Developed Random Walk Takes User's Preference and Item's Genres

It is difficult to quickly find the information that users are most interested in due to the increasing number and diversity of information. The traditional random walk algorithm only considers all the users' choice of items and ignores the user and item context information. This paper proposes an improved recommendation algorithm based on random walk algorithm, which takes user and item context information into account. In addition, an offset factor is introduced on the basis of this algorithm. The results show that the accuracy of the two algorithms is superior to the traditional random walk and cooperative filtering algorithms in the position of the top ranking.