Ride Ratings: Aggregate and Rank Drivers by Weighted Ride Rating Score

Question

## Problem

You are given a list of ride records, each with a driver ID, a passenger rating (1-5), and a timestamp. Compute each driver's weighted average rating where more recent rides are weighted more heavily using exponential decay: `weight = exp(-lambda * days_since_ride)`. Return drivers ranked by weighted rating descending.

```python
from datetime import datetime

def rank_drivers(
    rides: list[dict],  # {driver_id, rating, timestamp: datetime}
    as_of: datetime,
    decay_lambda: float = 0.01
) -> list[tuple[str, float]]:  # [(driver_id, weighted_rating), ...]
    pass
```

**Example:**
```
rides = [
  {"driver_id": "D1", "rating": 5, "timestamp": datetime(2025,1,1)},
  {"driver_id": "D1", "rating": 2, "timestamp": datetime(2025,3,1)},
  {"driver_id": "D2", "rating": 4, "timestamp": datetime(2025,3,15)},
]
as_of = datetime(2025, 4, 1)
# D1 recent low-rating pulls average down; D2 recent good rating wins
```

## Follow-ups

1. What is the intuition behind exponential decay weighting, and how do you choose the decay constant?
2. How would you compute this efficiently in SQL using window functions instead of Python?
3. What edge case arises when a driver has only one ride, and how do you avoid ranking bias?
4. How would you extend this to also penalize drivers with fewer total rides (Bayesian smoothing)?

Ride Ratings: Aggregate and Rank Drivers by Weighted Ride Rating Score

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