Cloudwalk has made a number of significant functional breakthroughs in this field:
The model can automatically search and mine features. With the search strategy manually configured, the model is able to automatically process and screen features to find the most effective feature combination, and apply the automatic feature engineering to mine feature combinations and bin the features, without manually selecting the features and adjusting the binning. Users only need to define the search range of model parameters, and the algorithm can automatically search and adjust the optimal model parameters. Using the decision-making tree for feature extraction, this model is characterized by strong nonlinear feature fitting ability. Experiments on multiple datasets show that with an effect similar to that of other models established based on the decision-making tree algorithm, e.g. LGB and XGB, this model can output the risk value corresponding to each feature and feature combination and then deploy these values through the decision-making engine.
Background and challenges
-
In offline retail scenarios, the inability to conduct precise personalized analysis on a large number of customers makes it difficult for everyone to be unique -
Due to a lack of understanding of customer habits, it is not possible to provide accurate marketing recommendations and standardize marketing content for a large number of customers -
Due to the inability to grasp the actual location of the customer, the spatial dimension is not focused and targeted push is not possible -
Due to the inability to understand the real-time requirements of customers and the lack of focus in the time dimension, precise matching is not possible when customers have needs -
Therefore, the trend of digital transformation for merchants is becoming increasingly evident, and there is an urgent need to connect online and offline, empower scenarios with AI, and upgrade smart stores
