Search Query
Search query optimization is a broad field aiming to improve the efficiency and effectiveness of information retrieval across diverse applications, from game playing and code generation to scientific literature exploration and medical image analysis. Current research focuses on developing novel algorithms, such as adaptive Monte Carlo Tree Search and various transformer-based architectures, to enhance search strategies and reduce computational costs. These advancements have significant implications for various fields, improving the speed and accuracy of tasks ranging from AI decision-making to large-scale data analysis and medical diagnosis.
Papers
Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites
Simiao Zuo, Qingyu Yin, Haoming Jiang, Shaohui Xi, Bing Yin, Chao Zhang, Tuo Zhao
Bflier's: A Novel Butterfly Inspired Multi-robotic Model in Search of Signal Sources
Chakravarthi J, Vinod Babu P, Pavan B, Ashok U, Marek Kolencik, Martin Šebesta, Ramakanth Illa
An AlphaZero-Inspired Approach to Solving Search Problems
Evgeny Dantsin, Vladik Kreinovich, Alexander Wolpert
SketchCleanNet -- A deep learning approach to the enhancement and correction of query sketches for a 3D CAD model retrieval system
Bharadwaj Manda, Prasad Kendre, Subhrajit Dey, Ramanathan Muthuganapathy