We propose a novel hashing-based matching scheme, called Locally Optimized Hashing (LOH), based on a state-of-the-art quantization algorithm that can be used for efficient, large-scal
Recommender systems are widely used for personalized recommendation in many business applications su...
It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal pro...
© 1992-2012 IEEE. Hash-based nearest neighbor search has become attractive in many applications. How...
We propose a novel hashing-based matching scheme, called Locally Optimized Hashing (LOH), based on ...
Recommender systems usually need to compare a large number of items before users\u27 most preferred ...
Abstract—Similarity search is critical for many database ap-plications, including the increasingly p...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Efficient high-dimensional similarity search structures are essential for building scalable content-...
Finding nearest neighbors has become an important operation on databases, with applications to text ...
International audienceIt is well known that high-dimensional nearest-neighbor retrieval is very expe...
Similarity search plays an important role in many applications involving high-dimensional data. Due ...
Similarity search plays an important role in many applications involving high-dimensional data. Due ...
In recent years, hashing techniques are becoming overwhelmingly popular for their high efficiency in...
Similarity search plays an important role in many applications involving high-dimensional data. Due ...
Recommender systems are widely used for personalized recommendation in many business applications su...
It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal pro...
© 1992-2012 IEEE. Hash-based nearest neighbor search has become attractive in many applications. How...
We propose a novel hashing-based matching scheme, called Locally Optimized Hashing (LOH), based on ...
Recommender systems usually need to compare a large number of items before users\u27 most preferred ...
Abstract—Similarity search is critical for many database ap-plications, including the increasingly p...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Efficient high-dimensional similarity search structures are essential for building scalable content-...
Finding nearest neighbors has become an important operation on databases, with applications to text ...
International audienceIt is well known that high-dimensional nearest-neighbor retrieval is very expe...
Similarity search plays an important role in many applications involving high-dimensional data. Due ...
Similarity search plays an important role in many applications involving high-dimensional data. Due ...
In recent years, hashing techniques are becoming overwhelmingly popular for their high efficiency in...
Similarity search plays an important role in many applications involving high-dimensional data. Due ...
Recommender systems are widely used for personalized recommendation in many business applications su...
It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal pro...
© 1992-2012 IEEE. Hash-based nearest neighbor search has become attractive in many applications. How...
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