面向微博用户的消费意图识别算法

The data set is constructed by the data of Jingdong Question Answer Platform and Weibo based on transfer learning method and a bi-directional long-term and short-term memory neural network model based on attention mechanism is proposed to identify users’ implicit consumption intention. For the problem of explicit intention recognition, a new algorithm for extracting consumer intention objects is proposed, which combines TF-IDF (term frequency-inverse document frequency) with the verb-object relationship (VOB) in parsing. The experimental results show that the training set can be effectively expanded by merging the data of Jingdong Question Answer Platform and Weibo. The classification model has high accuracy and recall rate, and the method of extracting explicit consumer intent objects by fusing VOB and TF-IDF achieves 78. 8% accuracy.