Abstract: Popular classification (Folksonomy) tag application has gradually become an important way of internet content organization,but with the massive increase in the scale of data,the problem of information overload has been produced.On the other hand,the traditional personalized recommendation algorithm based on the relationship between ‘user-item’ is difficult to have effect on the three elements of the “user-item-label”.Based on the improvement of basic artificial fish swarm algorithm,a clustering analysis method was proposed for the initial data set of the tag recommendation system(TRS),which is used to reduce the scale of the data analysis of the TRS.Based on this,through comprehensive consideration of the label recommendation system element weights and the reflection of user preference score information,and by weighted processing of the element weights and grades as the elements in the tensor,a new weighted tensor model was established,and the model was solved by the dynamic incremental updating of the tensor decomposition algorithm,completing the personalized recommendation.Finally,on two real experimental data sets,the proposed algorithm (FTA) and the other two classic tag recommendation algorithms were compared and analyzed.The experimental results show that the FTA algorithm has better performance in the recall rate and precision rate.

Key words: Artificial fish swarm algorithm,Clustering analysis,Tensor decomposition,Tag recommendation