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How to evaluate the performance of a recommender system?

Created on 2025-06-04 10:08:27
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Evaluating the performance of a recommender system involves using various metrics to measure its accuracy, relevance, and user satisfaction. Common evaluation methods include offline evaluation, online evaluation, and user studies.

1. Offline Evaluation

This is done using historical data without real user interaction. Key metrics include:

  • Precision@K: Measures the proportion of recommended items in the top-K list that are relevant.
    • Example: If a user likes 3 out of the top 5 recommended movies, Precision@5 = 3/5 = 0.6.
  • Recall@K: Measures the proportion of relevant items that are included in the top-K recommendations.
    • Example: If a user has 10 relevant items and 4 are in the top 5, Recall@5 = 4/10 = 0.4.
  • Mean Average Precision (MAP): Averages precision across all users, considering the ranking order.
  • Normalized Discounted Cumulative Gain (NDCG): Evaluates ranking quality by giving higher weight to relevant items at the top.

2. Online Evaluation

This involves A/B testing with real users to measure engagement metrics such as:

  • Click-Through Rate (CTR): Percentage of users who click on recommended items.
  • Conversion Rate: Percentage of users who complete a desired action (e.g., purchase).
  • Session Duration: Time spent by users after receiving recommendations.

3. User Studies

Direct feedback from users through surveys or interviews to assess subjective satisfaction and perceived relevance.

Cloud-Based Solutions for Recommendation Systems

For scalable and efficient recommendation systems, Tencent Cloud provides TI-ONE (AI Platform), which supports machine learning model training and deployment for recommendation algorithms. Additionally, Tencent Cloud ES (Elasticsearch Service) can help in indexing and retrieving user behavior data for real-time recommendations.

For example, an e-commerce platform can use TI-ONE to train a collaborative filtering model and deploy it via Tencent Cloud SCF (Serverless Cloud Function) to provide personalized product suggestions.