Sun Kaiwei

TL;DR: In this article, a commodity recommendation method based on mobile electronic commerce of big data is proposed, where the historical data of the user is preprocessed and analyzed to extract features, the plurality of machine learning models are established so as to predict a probability for the user to purchase the certain commodity in one future day, and accuracy for a merchant to recommend commodities to the user was improved.

TL;DR: In this paper, an active learning based data automatic marking method was proposed, which belongs to the field of active learning and self-checking for data automatic data marking, where the goal is to ensure the accuracy of manually marked data as far as possible while reducing the volume of the manually annotated data.

TL;DR: In this paper, the authors proposed a high potential user buying intention prediction method based on big data user behavior analysis, which comprises the following steps: 101 data preprocessing: the historical behavior data set of the e-commerce user is preprocessed; 102 sample defining and marking: samples are constructed with the interacted user product pairs to act as the keywords according to the historical consumption behavior of the user.

TL;DR: In this article, a default user risk prediction method based on big data finance was proposed, which mainly pretreats and analyzes the historical data of users, extracts features, selects features, establishes a plurality of machine learning models, predicts whether users will overpay in the next month according to the consumer behavior data, and provides more accurate risk control service for subdividing crowds in the financial field.

TL;DR: In this article, a man-machine conversation intention identification method in the financial field based on big data is proposed, which comprises the following steps: 101, preprocessing text data generated by the manmachine conversation in the Financial field; 102, partitioning a given set of text data; 103, according to the data of manmachine conversations, constructing the feature of text features, including feature extraction and text vectorization; 104, carrying out dimension reduction and sparse processing according to features after construction; 105, for text data, establishing a machine learning model to recognize the intention of unknown