Start by clarifying the data representation: are themes lists of tokens (e.g., color names, keywords) or sets? Then implement Jaccard similarity as intersection over union, and apply a threshold to flag likely pirated themes. Explain that the threshold should be chosen based on business trade-offs between false positives and false negatives, ideally validated with labeled data or domain knowledge.
Pro tip: Mention that Jaccard similarity is sensitive to list length and tokenization; consider normalizing or using weighted Jaccard if themes have varying sizes. Also, propose a data-driven threshold selection using precision-recall curves if labels are available.
Ask clarifying questions about the format of themes (lists of strings, sets, etc.), whether order matters, and what 'pirated' means in this context. Confirm the output: a flag per custom theme.
Write a Python function that takes two lists, converts them to sets, and returns the size of intersection divided by size of union. Handle edge cases like empty lists.
For each custom theme, compute similarity against all known pirate themes, take the maximum similarity, and flag if it exceeds a threshold. Return a list of flags or a DataFrame with scores.
Select a threshold (e.g., 0.7) based on business impact: higher threshold reduces false positives but may miss subtle piracy. Discuss using labeled data to optimize via F1-score or precision-recall trade-off.
Acknowledge that Jaccard ignores frequency and order; suggest alternatives like cosine similarity on TF-IDF or weighted Jaccard if needed. Mention scalability for large lists.
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