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This is a medium difficulty Machine Learning question, commonly asked during Onsite rounds at Meta.

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This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Determine Features for Effective Hashtag Recommendations

Last updated: Mar 29, 2026

Quick Overview

This question evaluates expertise in feature engineering and ranking for recommender systems, specifically assessing skills in signal selection, cold-start strategies, scoring functions, and parameter learning for hashtag recommendations.

Hashtag Recommendation System Design

Context

You are designing a hashtag recommendation system for a social-media platform. Given a user u composing a post with draft content c at time t, the system should rank and recommend the top-k hashtags.

Tasks

Signals/Features: What signals would you collect to recommend hashtags for a given (u, c, t)? Group them logically (e.g., engagement, content similarity, social/graph, demographics, popularity/trends).
Cold Start: For users or hashtags where those features are unavailable or uninformative (e.g., new users or new hashtags), how would you handle recommendations?
Scoring Function: How would you combine the collected features into a scoring function that produces a ranked list of hashtags?
Weight/Parameter Learning: How would you determine or learn the weights for each feature? Discuss both offline and online approaches.

Solution

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