We present the first provably sublinear time hashing algorithm for approximate\emph
{Maximum Inner Product Search}(MIPS). Searching with (un-normalized) inner product as …

Approximate nearest neighbor (ANN) searching is a fundamental problem in computer
science with numerous applications in (eg,) machine learning and data mining. Recent …

We consider the problem of designing locality sensitive hashes (LSH) for inner product
similarity, and of the power of asymmetric hashes in this context. Shrivastava and Li (2014a) …

Point-of-Interest (POI) recommendation has become an important means to help people
discover attractive and interesting places, especially when users travel out of town …

We present a fast search on graph algorithm for Maximum Inner Product Search (MIPS). This
optimization problem is challenging since traditional Approximate Nearest Neighbor (ANN) …

Recommender systems have many successful applications in e-commerce and social
media, including Amazon, Netflix, and Yelp. Matrix Factorization (MF) is one of the most …

A large class of commonly used probabilistic models known as log-linear models are
defined up to a normalization constant. Typical learning algorithms for such models require …

The problem of Approximate Maximum Inner Product (AMIP) search has received increasing
attention due to its wide applications. Interestingly, based on asymmetric transformation, the …

Model-based methods for recommender systems have been studied extensively for years.
Modern recommender systems usually resort to 1) representation learning models which …

Recently, plenty of neural network based recommendation models have demonstrated their
strength in modeling complicated relationships between heterogeneous objects (ie, users …