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MatrixFactorization with Giraph

machine learning

chezou のブックマーク 2015/06/05 11:44

<blockquote class="hatena-bookmark-comment"><a class="comment-info" href="https://b.hatena.ne.jp/entry/254546481/comment/chezou" data-user-id="chezou" data-entry-url="https://b.hatena.ne.jp/entry/s/engineering.fb.com/2015/06/02/core-infra/recommending-items-to-more-than-a-billion-people/" data-original-href="https://engineering.fb.com/2015/06/02/core-infra/recommending-items-to-more-than-a-billion-people/" data-entry-favicon="https://cdn-ak2.favicon.st-hatena.com/64?url=https%3A%2F%2Fengineering.fb.com%2F2015%2F06%2F02%2Fcore-infra%2Frecommending-items-to-more-than-a-billion-people%2F" data-user-icon="/users/chezou/profile.png">Recommending items to more than a billion people</a><ul class="comment-tag" style="list-style: none; margin: 0px;"><li style="float: left">[<a href="https://b.hatena.ne.jp/q/%22machine%20learning%22">machine learning</a>]</li></ul><br><p style="clear: left"> MatrixFactorization with Giraph</p><a class="datetime" href="https://b.hatena.ne.jp/chezou/20150605#bookmark-254546481"><span class="datetime-body">2015/06/05 11:44</span></a></blockquote><script src="https://b.st-hatena.com/js/comment-widget.js" charset="utf-8" async></script>

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Recommending items to more than a billion people

engineering.fb.com2015/06/03

The growth of data on the web has made it harder to employ many machine learning algorithms on the full data sets. For personalization probl ems in particular, where data sampling is often not an op...

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