Bipartite Graph Recommendation Algorithm Based on Hybrid User Model
Huang Ta · Computer Technology and Development · 2014
With the bipartite graph to achieve personalized recommendation algorithm has received more and more attention of researchers.Present bipartite graph recommendation algorithm based on the hybrid user model( MNBI),aiming at cases of the bipartite graph recommendations algorithm in the presence of multiple users,low project,use hybrid user model to improve,at the same time for the bipartite graph recommendations of weighted edge weights for users to have the overall weighted improved.The basic idea of the algorithm is,using hybrid user model to make a pretreatment for user,generating a certain number of user set when the number of the users is huge,and then use the user sets and project set to construct user-figure two bipartite graph.By focusing on the Movielens data to test the experimental results show that,compared with NBI algorithm,MNBI algorithm recommended hit efficiency is improved,at the same time for recommendation diversity increased,and has good effect in the user data cold start conditions.