Detecting Spammy Friend Requests

Context

Assume a consumer social platform where users can send friend requests (optionally with a short message). The goal is to protect user experience by reducing spammy requests while preserving legitimate connections, especially for new users.

Tasks

1. Definition
   • Clearly define what constitutes a "spammy" friend request on this platform.
2. Feature Engineering
   • List the key features/signals you would engineer to identify spammy requests (sender, recipient, pairwise, graph, content, device, temporal, and feedback signals).
3. Modeling Approach
   • Describe how you would build an initial classification model, including data splitting, handling class imbalance, model choice, interpretability, and calibration.
4. Labels When None Exist
   • If no labeled data exist, explain strategies to obtain/generate labels (heuristics, human-in-the-loop, semi-/weak supervision, and positive–unlabeled learning).
5. Product Integration
   • Once live, how would you use the model to improve user experience (interventions and experimentation)?
6. Precision–Recall Trade-off
   • How would you determine, set, and monitor the appropriate precision–recall trade-off for this problem?