In order to solve the accuracy problem of film and television resources recommendation,the latent Dirichlet allocation(LDA)is introduced to complete the analysis of film and television works review data.Considering that LDA can not reflect the importance of each feature word,the attention mechanism is embedded into the network to improve model accuracy.The re-sults show that in the recommendation accuracy test,by the MovieLens-1M data,the proposed model has an accuracy of 0.936,which is the best compared to similar technologies in terms of accuracy.From this,it can be seen that the proposed rec-ommendation model has excellent application results in system stability and recommendation effectiveness.
关键词
推荐算法/影视作品/LDA/注意力机制
Key words
recommendation algorithm/film and television work/LDA/attention mechanism
维普
万方数据