This package provides several LSH-base schemes for k-Maximum Inner Product Search (k-MIPS). It is also an implementation of H2_ALSH from the paper as follows:
Qiang Huang, Guihong Ma, Jianlin Feng, Qiong Fang, and Anthony K. H. Tung. Accurate and Fast
Locality-Sensitive Hashing Scheme for Maximum Inner Product Search. In Proceedings of the 24th
ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining, pages 1561-1570, 2018.The package requires g++ with c++11 support. To download and compile the code, type:
git clone [email protected]/HuangQiang/H2_ALSH.git
cd H2_ALSH
makeWe use four real-life datasets Sift, Gist, Netflix, and Yahoo for comparison. The statistics of the datasets are summarized in the following table:
| Datasets | #Objects | #Queries | Dimensionality | Data Size |
|---|---|---|---|---|
| Sift | 1,000,000 | 1,000 | 128 | 512.0 MB |
| Netflix | 17,770 | 1,000 | 300 | 21.3 MB |
| Yahoo | 624,961 | 1,000 | 300 | 750.0 MB |
| Gist | 1,000,000 | 1,000 | 960 | 3.8 GB |
Usage: alsh [OPTIONS]
This package supports 12 options to evaluate the performance of H2_ALSH, L2_ALSH,
L2_ALSH2, XBOX, Sign_ALSH, Simple_LSH and Linear_Scan for k-MIPS. The parameters
are introduced as follows.
-alg integer options of algorithms (0 - 11)
-n integer cardinality of dataset
-d integer dimensionality of dataset and query set
-qn integer number of queries
-K integer number of hash tables for Sign_ALSH and Simple_LSH
-m integer extra dimension for L2_ALSH, L2_ALSH2, and Sign_ALSH
-U float a value in (0,1] for L2_ALSH, L2_ALSH2, and Sign_ALSH
-c0 float approximation ratio for NN Search (c0 > 1)
-c float approximation ratio for MIP Search (0 < c < 1)
-ds string address of data set
-qs string address of query set
-ts string address of truth set
-op string output pathWe provide all scripts to repeat all experiments reported in SIGKDD 2018. A quick example is shown as follows (run H2_ALSH on Mnist):
./alsh -alg 1 -n 60000 -qn 1000 -d 50 -c0 2.0 -c 0.5 -ds data/Mnist/Mnist.ds -qs data/Mnist/Mnist.q -ts data/Mnist/Mnist.mip -op results/Mnist/If you would like to get more information to run other algorithms, please check the scripts in the package. When you run the package, please ensure that the path for the dataset, query set, and truth set is correct. Since the package will automatically create folder for the output path, please keep the path as short as possible.
If you use this package for publications, please cite the paper as follows.
@inproceedings{huang2018accurate,
title={Accurate and Fast Asymmetric Locality-Sensitive Hashing Scheme for Maximum Inner Product Search}
author={Huang, Qiang and Ma, Guihong and Feng, Jianlin and Fang, Qiong and Tung, Anthony KH},
booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={1561--1570},
year={2018},
organization={ACM}
}
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