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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">SimHash</span></span>
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<p>In <a href="Computer_science" title="Computer science">computer science</a>, <b>SimHash</b> is a technique for quickly estimating how <a href="Similarity_measure" title="Similarity measure">similar</a> two sets are. The <a href="Algorithm" title="Algorithm">algorithm</a> is used by the <a href="Google" title="Google">Google</a> <a href="Web_crawler" title="Web crawler">Crawler</a> to find near duplicate pages. It was created by <a href="Moses_Charikar" title="Moses Charikar">Moses Charikar</a>. In 2021 Google announced its intent to also use the algorithm in their newly created <a href="Federated_Learning_of_Cohorts" title="Federated Learning of Cohorts">FLoC (Federated Learning of Cohorts)</a> system.<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup>
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<div class="mw-heading mw-heading2"><h2 id="Evaluation_and_benchmarks">Evaluation and benchmarks</h2></div>
<p>A large scale evaluation has been conducted by <a href="Google" title="Google">Google</a> in 2006<sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> to compare the performance of <a href="Minhash" class="mw-redirect" title="Minhash">Minhash</a> and Simhash<sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> algorithms. In 2007 Google reported using Simhash for duplicate detection for web crawling<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> and using Minhash and <a href="Locality-sensitive_hashing" title="Locality-sensitive hashing">LSH</a> for <a href="Google_News" title="Google News">Google News</a> personalization.<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup>
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<div class="mw-heading mw-heading2"><h2 id="See_also">See also</h2></div>
<ul><li><a href="MinHash" title="MinHash">MinHash</a></li>
<li><a href="W-shingling" title="W-shingling">w-shingling</a></li>
<li><a href="Count%E2%80%93min_sketch" title="Count–min sketch">Count–min sketch</a></li>
<li><a href="Locality-sensitive_hashing" title="Locality-sensitive hashing">Locality-sensitive hashing</a></li></ul>
<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>
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<li id="cite_note-1"><span class="mw-cite-backlink"><b><a href="#cite_ref-1">^</a></b></span> <span class="reference-text"><style data-mw-deduplicate="TemplateStyles:r1238218222">
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</style><cite id="CITEREFCyphers2021" class="citation web cs1">Cyphers, Bennett (2021-03-03). <a rel="nofollow" class="external text" href="https://www.eff.org/deeplinks/2021/03/googles-floc-terrible-idea">"Google's FLoC Is a Terrible Idea"</a>. <i>Electronic Frontier Foundation</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2021-04-13</span></span>.</cite></span>
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<li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text">
<cite id="CITEREFHenzinger2006" class="citation cs2">Henzinger, Monika (2006), "Finding near-duplicate web pages: a large-scale evaluation of algorithms", <a rel="nofollow" class="external text" href="http://infoscience.epfl.ch/record/99373"><i>Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval</i></a>, p. 284, <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1145%2F1148170.1148222">10.1145/1148170.1148222</a>, <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-1595933690</bdi>, <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:207160068">207160068</a></cite>.</span>
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<li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text"><cite id="CITEREFCharikar,_Moses_S.2002" class="citation cs2">Charikar, Moses S. (2002), "Similarity estimation techniques from rounding algorithms", <i>Proceedings of the 34th Annual ACM Symposium on Theory of Computing</i>, pp. <span class="nowrap">380–</span>388, <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1145%2F509907.509965">10.1145/509907.509965</a>, <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-1581134957</bdi>, <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:4229473">4229473</a></cite>.</span>
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<li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text">
<cite id="CITEREFGurmeet_SinghJainDas_Sarma2007" class="citation cs2">Gurmeet Singh, Manku; Jain, Arvind; Das Sarma, Anish (2007), "Detecting near-duplicates for web crawling", <a rel="nofollow" class="external text" href="http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/33026.pdf"><i>Proceedings of the 16th International Conference on World Wide Web</i></a> <span class="cs1-format">(PDF)</span>, p. 141, <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1145%2F1242572.1242592">10.1145/1242572.1242592</a>, <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>9781595936547</bdi></cite>.</span>
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<li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text">
<cite id="CITEREFDasDatarGargRajaram2007" class="citation cs2">Das, Abhinandan S.; Datar, Mayur; Garg, Ashutosh; Rajaram, Shyam; et al. (2007), "Google news personalization: scalable online collaborative filtering", <i>Proceedings of the 16th International Conference on World Wide Web</i>, p. 271, <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1145%2F1242572.1242610">10.1145/1242572.1242610</a>, <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>9781595936547</bdi>, <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:207163129">207163129</a></cite>.</span>
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<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>
<ul><li><a rel="nofollow" class="external text" href="http://www.cs.princeton.edu/courses/archive/spring04/cos598B/bib/CharikarEstim.pdf">Simhash Princeton Paper</a></li>
<li><a rel="nofollow" class="external text" href="http://matpalm.com/resemblance/simhash/">Simhash explained</a></li>
<li><a rel="nofollow" class="external text" href="http://proceedings.mlr.press/v33/shrivastava14.pdf">Comparison of MinHash vs. Simhash</a></li></ul>
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</style><div id="Machine_learning_evaluation_metrics317" style="font-size:114%;margin:0 4em"><a href="Machine_learning" title="Machine learning">Machine learning</a> evaluation metrics</div></th></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Regression_analysis" title="Regression analysis">Regression</a></th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">
<ul><li><a href="Mean_squared_error" title="Mean squared error">MSE</a></li>
<li><a href="Mean_absolute_error" title="Mean absolute error">MAE</a></li>
<li><a href="Symmetric_mean_absolute_percentage_error" title="Symmetric mean absolute percentage error">sMAPE</a></li>
<li><a href="Mean_absolute_percentage_error" title="Mean absolute percentage error">MAPE</a></li>
<li><a href="Mean_absolute_scaled_error" title="Mean absolute scaled error">MASE</a></li>
<li><a href="Mean_squared_prediction_error" title="Mean squared prediction error">MSPE</a></li>
<li><a href="Root_mean_square" title="Root mean square">RMS</a></li>
<li><a href="Root_mean_square_deviation" title="Root mean square deviation">RMSE/RMSD</a></li>
<li><a href="Coefficient_of_determination" title="Coefficient of determination">R<sup>2</sup></a></li>
<li><a href="Mean_directional_accuracy" title="Mean directional accuracy">MDA</a></li>
<li><a href="Median_absolute_deviation" title="Median absolute deviation">MAD</a></li></ul>
</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Statistical_classification" title="Statistical classification">Classification</a></th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">
<ul><li><a href="F-score" title="F-score">F-score</a></li>
<li><a href="P4-metric" title="P4-metric">P4</a></li>
<li><a href="Accuracy_and_precision" title="Accuracy and precision">Accuracy</a></li>
<li><a href="Precision_and_recall" title="Precision and recall">Precision</a></li>
<li><a href="Precision_and_recall" title="Precision and recall">Recall</a></li>
<li><a href="Cohen's_kappa" title="Cohen's kappa">Kappa</a></li>
<li><a href="Phi_coefficient" title="Phi coefficient">MCC</a></li>
<li><a href="Receiver_operating_characteristic#Area_under_the_curve" title="Receiver operating characteristic">AUC</a></li>
<li><a href="Receiver_operating_characteristic" title="Receiver operating characteristic">ROC</a></li>
<li><a href="Sensitivity_and_specificity" title="Sensitivity and specificity">Sensitivity and specificity</a></li>
<li><a href="Cross-entropy#Cross-entropy_loss_function_and_logistic_regression" title="Cross-entropy">Logarithmic loss</a></li></ul>
</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Cluster_analysis" title="Cluster analysis">Clustering</a></th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">
<ul><li><a href="Silhouette_(clustering)" title="Silhouette (clustering)">Silhouette</a></li>
<li><a href="Calinski%E2%80%93Harabasz_index" title="Calinski–Harabasz index">Calinski–Harabasz index</a></li>
<li><a href="Davies%E2%80%93Bouldin_index" title="Davies–Bouldin index">Davies–Bouldin index</a></li>
<li><a href="Dunn_index" title="Dunn index">Dunn index</a></li>
<li><a href="Hopkins_statistic" title="Hopkins statistic">Hopkins statistic</a></li>
<li><a href="Jaccard_index" title="Jaccard index">Jaccard index</a></li>
<li><a href="Rand_index" title="Rand index">Rand index</a></li>
<li><a href="Similarity_measure" title="Similarity measure">Similarity measure</a></li>
<li><a href="Simple_matching_coefficient" title="Simple matching coefficient">SMC</a></li>
<li><a href="DBCV_index" class="mw-redirect" title="DBCV index">DBCV index</a></li></ul>
</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Ranking_(information_retrieval)" title="Ranking (information retrieval)">Ranking</a></th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">
<ul><li><a href="Mean_reciprocal_rank" title="Mean reciprocal rank">MRR</a></li>
<li><a href="NDCG" class="mw-redirect" title="NDCG">NDCG</a></li>
<li><a href="Average_precision" class="mw-redirect" title="Average precision">AP</a></li></ul>
</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Computer_vision" title="Computer vision">Computer vision</a></th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">
<ul><li><a href="PSNR" class="mw-redirect" title="PSNR">PSNR</a></li>
<li><a href="SSIM" class="mw-redirect" title="SSIM">SSIM</a></li>
<li><a href="Intersection_over_union" class="mw-redirect" title="Intersection over union">IoU</a></li></ul>
</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Natural_language_processing" title="Natural language processing">NLP</a></th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">
<ul><li><a href="Perplexity" title="Perplexity">Perplexity</a></li>
<li><a href="BLEU" title="BLEU">BLEU</a></li></ul>
</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Deep_learning" title="Deep learning">Deep learning</a></th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">
<ul><li><a href="Inception_score" title="Inception score">Inception score</a></li>
<li><a href="Fr%C3%A9chet_inception_distance" title="Fréchet inception distance">FID</a></li></ul>
</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Recommender_system" title="Recommender system">Recommender system</a></th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">
<ul><li><a href="Coverage_probability" title="Coverage probability">Coverage</a></li>
<li>Intra-list similarity</li></ul>
</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Similarity</th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">
<ul><li><a href="Cosine_similarity" title="Cosine similarity">Cosine similarity</a></li>
<li><a href="Euclidean_distance" title="Euclidean distance">Euclidean distance</a></li>
<li><a href="Pearson_correlation_coefficient" title="Pearson correlation coefficient">Pearson correlation coefficient</a></li></ul>
</div></td></tr><tr><td class="navbox-abovebelow hlist" colspan="2"><div>
<ul><li><a href="Confusion_matrix" title="Confusion matrix">Confusion matrix</a></li></ul>
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