Simulation of Multi-Attribute Information under Recurrent Neural Network
At present,information recommendation has become the key method to solve the problem of information overload in current network resource interaction.However,the data structure of multi-attribute information is complex,so the single-attribute data recommendation algorithm is difficult to achieve ideal accuracy and results.With consideration of the form and propagation path of user trust information,this paper presented a fuzzy recommendation algorithm for multi-attribute information based on a recursive neural network.Firstly,the recursive neural network was used to store the historical interest information.Then,a scoring matrix was established by the user·s interest in the project.Based on the cosine similarity,the attribute similarity,comprehensive similarity and user similarity between projects were calculated.Moreover,the weight factor was introduced to linearly integrate the prediction scores of com-prehensive similarity and user similarity and thus to obtain a multi-attribute information recommendation list.Finally,top-N was selected to recommend to users.Experimental results show that the ILS of similarity index is always less than 0.3,and the recommended accuracy is higher than 90%.Meanwhile,the average absolute error can be controlled within 0.2.The above data prove that the proposed method can comprehensively consider the multi-attribute features of the target and recommend the object with the highest degree of interest for users.