18474907. Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods simplified abstract (GOOGLE LLC)
Contents
- 1 Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods
Organization Name
Inventor(s)
Philip Wenjie Sun of New York NY (US)
Sanjiv Kumar of Jericho NY (US)
Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods - A simplified explanation of the abstract
This abstract first appeared for US patent application 18474907 titled 'Tuning Approximate Nearest Neighbor Search Engines for Speed-Recall Tradeoffs Via Lagrange Multiplier Methods
Simplified Explanation
The disclosure is directed towards automatically tuning quantization-based approximate nearest neighbors (ANN) search methods and systems (e.g., search engines) to perform at the speed-recall pareto frontier. With a desired search cost or recall as input, the embodiments employ Lagrangian-based methods to perform constrained optimization on theoretically-grounded search cost and recall models. The resulting tunings, when paired with the efficient quantization-based ANN implementation of the embodiments, exhibit excellent performance on standard benchmarks while requiring minimal tuning or configuration complexity.
- Efficient tuning of quantization-based approximate nearest neighbors (ANN) search methods and systems.
- Lagrangian-based methods used for constrained optimization on search cost and recall models.
- Excellent performance on standard benchmarks with minimal tuning or configuration complexity.
Potential Applications
The technology can be applied in various fields such as:
- Information retrieval systems
- Image and video search engines
- Recommendation systems
Problems Solved
- Improving the efficiency and accuracy of approximate nearest neighbors search methods.
- Reducing the complexity of tuning and configuration in search engines.
Benefits
- Enhanced performance on standard benchmarks.
- Minimal tuning or configuration complexity required.
- Improved speed-recall trade-off in search engines.
Potential Commercial Applications
Optimizing search engines for:
- E-commerce platforms
- Social media networks
- Online advertising companies
Possible Prior Art
One possible prior art could be the use of Lagrangian-based methods in optimization algorithms for search engines.
Unanswered Questions
How does the technology compare to other optimization methods in terms of performance and efficiency?
The article does not provide a direct comparison with other optimization methods to evaluate the superiority of the proposed technology.
Are there any limitations or constraints in the implementation of this technology?
The article does not mention any potential limitations or constraints that may arise during the implementation of the technology.
Original Abstract Submitted
The disclosure is directed towards automatically tuning quantization-based approximate nearest neighbors (ANN) search methods and systems (e.g., search engines) to perform at the speed-recall pareto frontier. With a desired search cost or recall as input, the embodiments employ Lagrangian-based methods to perform constrained optimization on theoretically-grounded search cost and recall models. The resulting tunings, when paired with the efficient quantization-based ANN implementation of the embodiments, exhibit excellent performance on standard benchmarks while requiring minimal tuning or configuration complexity.
