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GUITAR: Gradient Pruning toward Fast Neural Ranking
With the continuous popularity of deep learning and representation learning, fast vector search becomes a vital task in various ranking/retrieval based applications, say recommendation, ads ranking and question answering.
Asymmetric Hashing for Fast Ranking via Neural Network Measures
Previous learning-to-hash approaches are also not suitable to solve the fast item ranking problem since they can take a significant amount of time and computation to train the hash functions.
Constrained Approximate Similarity Search on Proximity Graph
Typically a three-stage mechanism is employed in those systems: (i) a small collection of items are first retrieved by (e. g.,) approximate near neighbor search algorithms; (ii) then a collection of constraints are applied on the retrieved items; (iii) a fine-grained ranking neural network is employed to determine the final recommendation.
Proximity Graph Maintenance for Fast Online Nearest Neighbor Search
Given a vertex deletion request, we thoroughly investigate solutions to update the connections of the vertex.
Möbius Transformation for Fast Inner Product Search on Graph
We present a fast search on graph algorithm for Maximum Inner Product Search (MIPS).
On Efficient Retrieval of Top Similarity Vectors
Retrieval of relevant vectors produced by representation learning critically influences the efficiency in natural language processing (NLP) tasks.
Fast Binary Functional Search on Graph
As Approximate Nearest Neighbor Search (ANNS) techniques have specifications on metric distances, efficient searching by advanced measures is still an open question.
