# User Quality (UQ) MemAgent

In addition to evaluating the quality of individual user uploads, we incorporate a measure of user consistency in our quality assessment model. Specifically, we compute a User Quality (UQ) Score, representing the user’s consistency in producing high-quality uploads over time. The UQ Score is calculated as an attention-weighted sum of historical Post Quality (PQ) Scores, where the attention weights follow a generalized sigmoid kernel.

<figure><img src="/files/Xgxl2j4JLUq2F7uX6HPh" alt="" width="563"><figcaption><p>User Quality ( Time / Upload  )</p></figcaption></figure>

Let $$\hat{s}(\mathbf{x}\_t, \mathbf{y}\_t)$$ represent the post quality score of upload $$(\mathbf{x}\_t, \mathbf{y}\_t)$$ at time $$t$$ over a time horizon $$T$$, and let $$\mathbf{w}(t)$$ denote the attention weight applied to $$\hat{s}(\mathbf{x}\_t, \mathbf{y}\_t)$$. The user quality score $$Q$$ is given by:

$$
s(u)= \frac{\sum\_{t=1}^{T} \hat{s}(\mathbf{x}\_t, \mathbf{y}*t) \cdot \mathbf{w}(t)}{\sum*{t=1}^{T} \mathbf{w}(t)}
$$

where $$u$$ denotes the user and $$\mathbf{w}(t)$$ is derived from a generalized sigmoid function:

$$
\mathbf{w}(t) = \frac{1}{1 + \exp(-k'' (t - \tau''))}
$$

This method assigns higher weights to quality scores at specific points in time, influenced by the choice of parameters $$k''$$ and $$\tau''$$, allowing for flexible prioritization within the user history. For instance, with a high $$k''$$ value and $$\tau''$$ positioned towards the end of the time horizon, the model will emphasize recent posts more heavily, giving less weight to older contributions. This approach thus balances a user’s historical quality contributions with their recent behavior, providing a comprehensive, time-aware assessment of user quality that reflects both long-term consistency and current engagement.

<figure><img src="/files/k1WULFwGh5OT5TE8lD7i" alt="" width="563"><figcaption><p>UQ distribution on a i.i.d subset of Users</p></figcaption></figure>


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