发明人: Jiahui Feng(冯家辉), Qihang Zhao(赵启航), Hefu Liu(刘和福), Wei Chen(陈伟)
- This algorithm was developed by members of the IOM Lab at the School of Management, USTC
- This is an end to end deep learning method Mcformer to utilize the customer clickstream data to predict the user purchase intention.
- We aim to utilize the customer clickstream data to predict the customer purchase intention, the scenes as follows:
- Introduction of Mcformer
- In order to deal with multi-dimension clickstream sequence data, we proposed an end-to-end deep learning model, named Multi-channel for purchase transformer (Mcformer), to predict the customers’ purchasing intention. Figure 1 shows the model architecture of Mcformer. This model composed by four parts: embedding layer, multi-transformer layer, cross fusion layer and output layer. Embedding layer is used to embed the sparse one-hot vectors of the behavior data to dense vectors. After that, multi-channels transformer identify intra-information of each sequence. Then the cross fusion layer is applied to identify the inter-information of different sequences. Finally, Mcformer output the result by the multilayer perceptron.
- sklearn
- pandas
- pytorch
- cuda
''' bash python train.py '''
Our data is the real world data from https://tianchi.aliyun.com/dataset/dataDetail?dataId=649, this dataset need to preprocessing, which need long time. If you need the data to verify our model, you could contact with us [email protected]
And if you want to use your data, you have to provide
- the number of items: ni
- the number of category:nc
- the number of types: nt
- the number of hour/minutes: nh
you need to modify the files as follows:
- cat_pad_unk = [[0,nc+1,nc+2], [0,nh+1,nh+2]]
- item_pad_unk =[[0,ni+1,ni+2], [0,nh+1,nh+2]]
- type_pad_unk =[[0,nt+1,nt+2], [0,nh+1,nh+2]]
the file location
if you want to visualize the training process ,you should change parameters.
- The result show that Mcformer get great performance in long sequence classification tasks.
- The beat parameters in the file named optimizpara, so you can use our best parameters directly.
- We have disclosed the model best parameters, due to the large file size, you can click the hyperlink to download:
- HyperLink:https://pan.baidu.com/s/10pkew7_tZgbdbjGm0XxDbA verification code:pxxk