Scaling Up All Pairs Similarity Search

Citations in the corpus

Maximum inner-product search using cone trees (2012, methodology): “Numerous techniques exists for nearest-neighbor search in Euclidean metric space (see surveys like [9]).”
From Frequency to Meaning: Vector Space Models of Semantics (2010, methodology): “15 Another approach is Brand’s (2006) incremental truncated SVD algorithm.16 Yet another approach is Gorrell’s (2006) Hebbian algorithm for incremental truncated SVD. Brand’s and Gorrell’s algorithms both introduce interesting new ways of handling missing values, instead of treating them as zero val”
Efficient Merging and Filtering Algorithms for Approximate String Searches (2008, methodology): “In this paper we consider several string similarity functions, including edit distance, Jaccard, Cosine, and Dice [2].”
Web-Scale Distributional Similarity and Entity Set Expansion (2009, background): “Bayardo et al. (2007) described a strategy that potentially reduces the cost even further by omit-ting the coordinates with the highest number of non-zero value.”
Efficient k-nearest neighbor graph construction for generic similarity measures (2011, methodology): “all pairs similarity search or similarity join [2, 22, 21].”
Efficient similarity joins for near duplicate detection (2008, methodology): “Algorithm 1 describes our ppjoin algorithm, an extension to the All-Pairs algorithm [3], to combine positional filtering and prefix-filtering.”
CrowdER: Crowdsourcing Entity Resolution (2012, methodology): “Since it is expensive to compute the similarity for every pair of records, research on similarity-based techniques [2, 5,26] mainly focuses on how to reduce the number of pairs evaluated.”
Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation (2019, methodology): “(Bayardo et al., 2007).”
One Permutation Hashing (2012, unknown): “, 2007; Henzinger, 2006), all-pair similarity (Bayardo et al., 2007), similarity join/record-linkage (Koudas & Srivastava, 2005), temporal correlation (Chien & Immorlica, 2005), etc.”
Fast locality-sensitive hashing (2011, background): “Likewise, popular operations such as near-duplicate detection [7, 28, 19], all-pairs similarity [6], similarity join/record-linkage [25], temporal correlation [10], are made easier.”
Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment (2015, background): “Later, because of its locality sensitive property [22], minhash became a widely used hash function for creating hash buckets leading to efficient algorithms for numerous applications including spam detection [6], collaborative filtering [4], news personalization [15], compressing social networks [13”
Data Matching (2012, methodology): “Real-time data matching has much in common with the functionality of text and Web search engines [21, 43, 303]: real-time and approximate matching and ranking of results.”