A Social Tagging Recommendation Model Based on Improved Artificial Fish Swarm Algorithm and Tensor Decomposition

Folksonomy Tag Application (FTA) has emerged as an important approach of Internet content organization. However, with the massive increase in the scale of data, the information overloading problem has been more severe. On the other hand, traditional personalized recommendation algorithms based on the interaction between “user-item” are not easy to extend to the three dimensional interface of “user-item-tag”. This paper proposes a clustering analysis method for the initial dataset of the Tag Recommendation System (TRS) based on the improvement of Artificial Fish Swarm Algorithm (AFSA). The method is used for dimension reduction of TRS datasets. To this end, considering the weight of the elements in TRS and the score that can reveal user preference, a novel weighted tensor model is established. And in order to complete the personalized recommendation, the model is solved by the tensor decomposition algorithm with dynamic incremental updating. Finally, a comparative analysis between the proposed FTA algorithm and the two classical tag recommendation algorithms is conducted based on two sets of empirical data. The experimental results show that the FTA algorithm has better performance in terms of the recall rate and precision rate.