Audio Content Discovery

Answer

Clarifying Questions

1. Access Method:

   • How will the search be accessed?
     • Determine whether the search will be accessed via voice commands, typed queries, or both. This affects the design of the input processing system.
   • Search Language:
     • Will the search support multiple languages, or will it be limited to English?

2. Data Volume and Scope:

   • How much text makes up the podcast search?
     • Understand the scale of transcripts and metadata to be indexed.
   • How many podcasts?
     • Estimate the number of podcasts to gauge storage and processing needs.

3. User Demographics:

   • Who uses this tool?
     • Identify if the primary users are podcast creators, consumers, or both to tailor the search experience.

4. Search Experience:

   • What kind of search experience do we want?
     • Decide between keyword-based searches or more conversational queries.

5. Current Discoverability:

   • How do people find podcasts now?
     • Analyze existing methods of podcast discovery to identify gaps and opportunities.

6. Technical Feasibility:

   • Is an ML solution really needed?
     • Assess whether a machine learning approach is necessary or if simpler methods suffice.

Assessing Requirements

• Metrics for Success:

  • Engagement: Measure the percentage of people staying on the platform from a previous visit.
  • Conversion: Track the percentage of users converting to a premium plan through A/B testing.
  • Search Effectiveness: Define a 'hit' as a search result where users stream content for more than 30 seconds.

• Evaluation Metrics:

  • MRR (Mean Reciprocal Rank): Evaluate the position of the first relevant result.
  • NDCG (Normalized Discounted Cumulative Gain): Measure the relevance of search results, weighted by position.

Solution

1. Keyword-Based Search:

   • BM25 Algorithm: Utilize this for fast, efficient keyword-based search.
   • Tools: Implement using OpenSearch or Elasticsearch.
   • Weighting: Give higher weight to metadata like titles.

2. Semantic Search:

   • Embeddings: Use BERT to embed transcript sentences into a semantic vector space.
   • Similarity Search: Store vectors in FAISS for efficient retrieval.

3. Hybrid Approach:

   • Combine Results: Use a heuristic or model to combine keyword and semantic search results.
   • Router Model: Implement a classifier to decide between keyword and semantic search based on query type.

4. Advanced Features:

   • Textual Features: Include metadata, popularity, and user behavior in ranking.
   • Pairwise Ranking: Use tree-based or neural network models for ranking.

Validation

• Offline Evaluation:

  • BM25 Tuning: Adjust parameters to optimize NDCG scores.

• A/B Testing:

  • Live Testing: Conduct A/B tests to evaluate the effectiveness of search result ordering.

Additional Concerns

• Latency: Ensure search results are delivered promptly.
• Safety: Implement measures to prevent inappropriate content from appearing in search results.