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CTR / CVR prediction model.
Given a user state vector and an ad embedding, predicts:
P(click | user, ad) → CTR estimate
P(convert | click, ad) → CVR estimate
Used at inference time to annotate recommended ads with human-interpretable
click and conversion probability estimates. The DQN Q-values handle the
*long-term* reward signal; the CTR model provides *immediate* interpretability.
"""
import torch
import torch.nn as nn
class CTRModel(nn.Module):
def __init__(self, state_dim: int, n_ads: int, ad_embed_dim: int = 16):
super().__init__()
self.ad_emb = nn.Embedding(n_ads, ad_embed_dim)
in_dim = state_dim + ad_embed_dim
self.net = nn.Sequential(
nn.Linear(in_dim, 64), nn.ReLU(),
nn.Linear(64, 32), nn.ReLU(),
nn.Linear(32, 2), # [logit_ctr, logit_cvr]
)
def forward(self, state: torch.Tensor, ad_ids: torch.Tensor) -> torch.Tensor:
"""
state : (batch, state_dim)
ad_ids : (batch,) or (1,) broadcast
returns: (batch, 2) — [p_click, p_convert]
"""
ad_feat = self.ad_emb(ad_ids)
x = torch.cat([state.expand(len(ad_ids), -1), ad_feat], dim=-1)
return torch.sigmoid(self.net(x))
@torch.no_grad()
def predict(self, state: torch.Tensor, ad_ids: list) -> list:
"""Return list of (p_click, p_convert) for each ad_id."""
ids = torch.tensor(ad_ids, dtype=torch.long)
out = self.forward(state, ids) # (n_ads, 2)
return [(float(row[0]), float(row[1])) for row in out]
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