Research and application of recommendation algorithm based on bidirectional attention model

Since the beginning of the 20th century, with the continuous development of computer technology, more and more people began to use the convenience of computer to improve efficiency, but just because a large number of Internet users continue to increase, there is also a situation of information overload, which will lead to some problems, such as the huge amount of data, the extraction and utilization of effective information will increase Difficulties. The second is the previous recommendation algorithm, most of which predict through the score, but if only through the score, it will not make full use of other data information in the data set.In order to make the experimental results more convincing, this experiment uses a recommendation algorithm based on two-way attention model. First, the movie attributes and user attributes are processed by Convolutional Neural Network (CNN), and then through the full connection layer and the built attention model, effective information is obtained. Finally, the predicted score is generated and compared with the real score, and the final result is obtained Fruit.This experiment uses the movielens data set, by changing the parameters to affect the experimental results, so as to determine the final value of each parameter. This experiment is a comparative experiment, the recommendation algorithm with two-way attention model is compared with many previous algorithms, and the final conclusion is drawn. The experimental results are expressed by RMSE and MAE. According to the index results, the recommendation algorithm proposed in this experiment has better performance.