Content deduplication: vector vs keyword approaches | Zbyszko Papierski

Summary

The video explores the challenges of handling content duplication in user-generated educational material, with a focus on improving search precision. The speaker discusses their journey from using machine learning-based vector search to a more efficient approach with traditional search engines like Elasticsearch and OpenSearch. While vector search provided promising results, its high cost led the team to adopt simpler, faster solutions without compromising accuracy. The speaker emphasizes the importance of balancing machine learning with traditional methods and highlights future potential improvements, including the integration of dense vector search.

Takeaways

• 😀 The challenge of duplicate content in search results was a significant issue at Brainly, particularly with user-generated educational queries.
• 😀 Initially, a machine learning-based vector search approach was used to tackle content duplication, specifically through cosine similarity of vector embeddings.
• 😀 Vector search showed promise in the early stages, but the performance and computational costs quickly became a concern due to high resource usage.
• 😀 Switching to Elasticsearch (and later OpenSearch) allowed for a much more cost-effective and faster solution compared to the machine learning approach.
• 😀 Balancing precision and recall in search results was a key challenge, especially when dealing with both long and short user-generated queries.
• 😀 The decision to pivot to a search engine like OpenSearch was influenced by the need for a practical, scalable solution that could handle the high volume of user queries.
• 😀 While vector search provided good results initially, it was deemed too expensive in terms of computational resources and slower compared to traditional search engines.
• 😀 The importance of iterative improvement was emphasized, as the team continuously refined their search strategy while keeping an eye on performance metrics and cost considerations.
• 😀 Despite the success with OpenSearch, the team remained open to revisiting machine learning and exploring dense vector search in the future to improve results further.
• 😀 The trade-off between precision (relevant results) and recall (covering all possible results) is a common and painful challenge in search systems, especially when striving to avoid duplicates.
• 😀 Overall, the team's strategy focused on leveraging the best tools for the job, combining machine learning insights with search engine technologies to address practical challenges like duplication and performance.

Q & A

What was the main challenge faced by the team when handling user-generated search queries?

The main challenge was dealing with duplicate content in the search results, particularly when user-generated queries were very similar or identical, which led to a poor user experience.

Why did the team initially consider using machine learning for the problem?

The team believed machine learning could help handle long, user-generated queries more effectively, as it seemed to be a good match for identifying duplicates in complex search scenarios.

What was the conclusion about the cost-effectiveness of vector search compared to traditional search engines?

The team found that vector search, although powerful, tends to be much more expensive than traditional search engines like Elasticsearch, which led them to reconsider its use for this particular project.

Did the team conduct a comparison between the machine learning approach and the traditional search method?

No, the team did not perform a thorough comparative test between the two approaches, though they acknowledged that some duplicates might have been identified by both methods.

How did the team address the issue of query length and its impact on duplicate detection?

The team found that longer queries were easier to manage when it came to duplicate detection, as they provided more context, though shorter, similar queries posed a greater challenge for balancing precision and recall.

What was the team's approach to balancing precision and recall in their search strategy?

The team acknowledged that focusing on precision would reduce recall and vice versa, which is a common issue in search optimization. They needed to carefully design their strategy to balance these two factors.

What future steps did the speaker foresee in improving the search system?

The speaker foresaw the possibility of incorporating machine learning and vector search in the future, but for now, the team was focused on refining their current search method using Elasticsearch.

Did the team achieve significant improvements with their new search strategy?

Yes, the team improved upon previous results after working for three weeks, surpassing the best results previously achieved with machine learning, which showed the effectiveness of their new approach.

What are the potential benefits of combining machine learning with traditional search methods?

Combining machine learning with traditional search methods could provide better accuracy in detecting duplicates and improving search relevance. The team remains open to experimenting with such combinations in the future.

Why did the team decide not to use the vector search engine for this particular project?

The team decided not to use the vector search engine in this project because the model they were using was slightly different, and they felt that traditional search methods with Elasticsearch provided better results for their needs at the time.