MIT+Fintech Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

Research Context:Boston Globe

• What they really care about?
  • Content Recommendation:想变得有趣然后留住customer
  • Dynamic User Segementation:
    • 什么是Dynamic User Segementation？
    • Person A :sports/ Person B:politics
    • create profiles, and use this profiles for targeting ads, offers.
    • Company want to figure out your interest and target you and hope you buy their product.
  • Optimize user experience: how to place articles at the front page?
• 

Our broad contribution : A novel neural network for modeling dynamic of user interests

Desired Output: A sample user’s meaningful content consumption journey

Indentify these latent user interests

Import Modeling Considerations

1. Input data is high dimensional: text data是非结构化的
2. 注意interest 是 customized化的
3. 注意时间的动态性， take time into account. last year you are interest in Politics,but this year your interest has changed
4. Interpretability: Explain why that is the case. Why we are recommending this to this person.

Why NN?

Advantages of NNs

• Flexibly model nonlinearities:
  • c.f. splines or kernels. The nonlinearities is estimated from data itself, it is not predecided.
• Computationally efficient to estimate
  • Pytorch/GPU等等好用

Disadvantages of NNs

• Black-box nature ,lack interpretability. 跟上节课说的一样，manager他们听不懂。

Our model: NNs + interpretability

Our model: Interpretable decomposition into user and content factors:Matrix factorization

First step:降维

one-hot embedding:sparse and giant

GloVe

$E_a$:user降维后的，nothing generalization.但$E_x$是download的，是general的。

$x_i^{t-1}$这里$t-1$表示的是时间，如$t-1$是previous week,$t-2$是2 weeks ago。

First 2 : Estimate a nonlinear hidden state for each user.$l_i^t$：just a linear combination

ReLU: simplest non-linearlarity used by NNs

• Pre-trainned embeddings + hidden state of a user at that time stop: Combined nonlinearly
  $$l_i^t=\sigma_1(W_l ·[E_x x_i^t;E_a a_i])$$

这里$\sigma_1$这个activiation function 是ReLU.

$W_l$:model parameter estimated from the data.

First 3: In corporate Dynamics in the hidden state $U_i^t$ current hidden state.

nonlinearity in this case is just softmax function.

softmax function convert numbers into probabilities.

At time t, 30% sports,40%politics…

3 step: Combine the user and content factors

4 step: minimize the loss fuction

resonstructed estimate is close to the acutal

Data

这个630好高哦。

Empirical Results:Roadmap

AUC

ROC

Ablation Studies

Test out the relative contribution of different elements of our model.

Economic Significance of Results

Data-driven content categorizations.

Future work:adaptive personalization

Adaptive: see what u do react to taht

Conclusion

Q&A

separate model for each person?

require lots of data. Don’t observe people most of time

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