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Found 2 papers, 0 papers with code
Improving Distributed Similarity Join in Metric Space with Error-bounded Sampling
To address the limitation, we propose SP-Join, an end-to-end framework to support distributed similarity join in metric space based on the MapReduce paradigm, which (i) employs an estimation-based stratified sampling method to produce pivots with quality guarantees for any sample size, and (ii) devises an effective cost model as the guideline to split the whole datasets into partition in map and reduce phases according to the sampled pivots.
Databases
Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering
Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them.
