Recommendation Capacity Building Based on Convolutional Neural Network Unified Search
With the development of the information age,recommendation systems are crucial in meeting users needs.However,existing systems suffer from issues such as information scarcity and insufficient mining of semantic relationships.This paper proposes a recommendation system based on convolutional neural network,which consists of heterogeneous information feature fusion module,local information recommendation module,global information recommendation module for multi label classifica-tion,and heterogeneous information search recommendation module.The system aims to improve the shortcomings of tradi-tional methods in search engine query recommendation.The experimental results show that on the MovieLens dataset,the ac-curacy of the system is 0.8879,the recall is 0.7958,and the root mean square error is 0.8531.In future research,the model can be improved by further optimizing its parameters,introducing more heterogeneous information sources,and considering us-ers feedback.
convolutional neural networkWeb searchdeep learningcontext information
万方数据
维普
