海量信息语义的适配推荐系统设计OA

In order to understand the deep meaning of user queries,extract contextual semantic features,and improve the timeliness and scene adaptability of recommendation results,an adaptation recommendation system for massive information semantic is designed.In the interface module of the system,a massive data collection interface is used to collect and recommend massive information related to the target from different data sources.In the storage module,the Hadoop distributed file system is used to store and collect massive information.In the adaptation recommendation module,the convolutional neural network(CNN)and bidirectional long short-term memory network(BiLSTM)are used to extract local and contextual semantic features from stored massive information.The deep target semantic features can be obtained by weighting and fusing two semantic features,so as to accurately understand the deep meaning of user queries.The adaptation score of target recommendations is calculated based on user preference vectors.The targets with high adaptability scores are selected as adaptive recommendation results to improve the timeliness and scenario adaptability of recommendation results.The experimental results show that the designed system can effectively extract deep semantic features of the target and complete adaptive recommendations.Under different levels of information sparsity,the normalized loss cumulative gain of the system's adaptive recommendation is relatively high,indicating a high accuracy of the adaptive recommendation.