1. Predictive Analysis by Leveraging Temporal User Behavior and User Embeddings
CIKM2018
Zheng Yongli

2. CIKM2018

3. Background

4. Abstract
1, porpose a time aware RNN model, TRNN, for predicting next user actions ,
Conversion prediction, Preferred product prediction from user behavior data.
2, TRNN embeddings provide an effective representation for solving practical tasks such as recommendation, user segmentation and predictive
analysis of business metrics

5. Dataset extracting user action logs from corporate websites
Source: desktop, smart phone, and tablet
user-agent-string, IP address with wildcard match and IP range match
Processing:
1, remove duplicate user actions that were
repeated within 5 seconds
2, flter out infrequent actions that occurred
less than 100 times during a month.

6. Method time-aware RNN model :
1, represent each user’s behavior log as an event sequence
2, two timestamp-based difference :
a) the current action and the last action :
tsess is the threshold for sessionizing behavior logs (6 hours
tmax is the timestamp for the last user action in the sequence.
b) the beginning of the session and the current action:

7. Output
action embeddings
one-hot representation

8. DeepCare
Pham T, Tran T, Phung D, et al. Deepcare: A deep dynamic memory model for predictive medicine [C]//Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, 2016:
Baytas I M, Xiao C, Zhang X, et al. Patient subtyping via time-aware LSTM networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017:
Pham，et al.，Deepcare: A deep dynamic memory model for predictive medicine，PAKDD, 2016

9. Time-Aware LSTM
Baytas, et al.，Patient subtyping via time-aware LSTM networks，SIGKDD, 2017

10. User Behavior Modeling
compute probability distribution:
loss：
optimize target:
Number of training examples
Length of S
next action:

11. Sequence-level Dropout

12. Experiments and Results
1, Prediction of Next User Action
Baselines: N -gram models : Bi-gram, Trigram DeepCare TLSTM
Experimental setting:
Train: 80% of the user event sequences
Test : 20% of the user event sequences

14. x-axis is embedding size and y-axis is the top-1 accuracy
1, Prediction of Next User Action
x-axis is embedding size and y-axis is the top-1 accuracy

15. Experimental setting:
2, Prediction of User Conversion
Experimental setting:
the label as 1: if the user will make a purchase after tT the label as 0: otherwise
a logistic regression model

16. 3, Preferred Mobile Applications

17. Experimental setting:
Select top-8 most popular applications from 18 applications
a logistic regression model with multinomial
for multi-class classification task
Acc-1 : predict the most preferred application
Acc-All: predicting a set of applications

18. Thanks!