Determine Features for Effective Hashtag Recommendations

Q: Determine Features for Effective Hashtag Recommendations

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.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

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.

Determine Features for Effective Hashtag Recommendations

Hashtag Recommendation System Design

Context

Tasks

Solution

Comments (0)

Determine Features for Effective Hashtag Recommendations

Overview

Hashtag Recommendation System Design

Context

Tasks

Solution

Comments (0)