## Problem You are building the content feed for a swipe-based app. Each user has a set of `seen_ids` (profiles already swiped) and a preference vector. Given a pool of candidate profiles with feature vectors, return the top `k` unseen candidates ranked by cosine similarity to the user's preference vector. Exclude already-seen profiles. ```python def recommend( user_pref: list[float], candidates: dict[str, list[float]], # id -> feature vector seen_ids: set[str], k: int ) -> list[str]: pass ``` ## Example ``` user_pref = [1.0, 0.0, 1.0] candidates = { "p1": [1.0, 0.0, 1.0], "p2": [0.0, 1.0, 0.0], "p3": [0.8, 0.1, 0.9], "p4": [1.0, 0.0, 0.5], } seen_ids = {"p2"} k = 2 Output: ["p1", "p3"] # cosine sim: p1=1.0, p3=~0.99, p4=~0.95; p2 excluded ``` ## Follow-ups 1. Cosine similarity is O(d) per candidate — how do you scale to millions of profiles? 2. How would you incorporate mutual-match signals (both users swiped right) into ranking? 3. How do you prevent a popularity bias where the same top profiles dominate all feeds?

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Swipe Left: Implement a Recommendation Filter for a Dating-App Style Feed

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Problem You are building the content feed for a swipe-based app. Each user has a set of seen_ids (profiles already swiped) and a preference vector. Given a pool of candidate profiles with feature vectors, return the top k unseen candidates ranked by cosine similarity to the user's preference vector. Exclude already-seen profiles. Example Follow-ups Cosine similarity is O(d) per candidate — how do you scale to millions of profiles? How would you incorporate mutual-match signals (both users swiped…

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This is a reported interview question from a palantir interview during the phone round.

It covers the following topics: Coding, Phone .

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Coding Phone

Swipe Left: Implement a Recommendation Filter for a Dating-App Style Feed

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