Optimised tags with time attenuation recommendation algorithm based on tripartite graphs network

Social recommendation has attracted increasing attention in recent years due to the potential value of social relations in recommender systems. Social tags play an important role in improving recommendation accuracy. However, garbage tags may lead to data matrix sparseness and affect the accuracy and performance of recommendation system. To optimise social tags in the recommendation system, tags are sorted by popularity ranking method with the time attention model in order to remove the garbage tags. The time attenuation model is used to consider the variation of tags with time change. Then a novel recommendation algorithm with optimised social tags is proposed based on complete tripartite graph network. This method considers the preference information of users and items and generates recommendation items for users based on collaborative filtering. Experimental results show that the proposed algorithm predicts recommendation items more accurately than other existing approaches.