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<time title="创建时间:2020-09-02 21:30:00 / 修改时间:21:32:36" itemprop="dateCreated datePublished" datetime="2020-09-02T21:30:00+08:00">2020-09-02</time>
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<p>最近有调研几篇知识图谱在推荐系统的应用的论文,在组内进行了分享。在这里写成文字版,给大家普及一下知识图谱的背景以及在推荐上的应用。如有错误,望指正。<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line">背景介绍(2012-2020)</span><br><span class="line">推荐场景发展</span><br><span class="line">1. Collaborative knowledge base embedding for recommender systems KDD, 2016, Microsoft</span><br><span class="line">2. Deep knowledge-aware network (DKN) 2018, ShanghaiJiaoTongU</span><br><span class="line">3. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems, 2018, ShanghaiJiaoTongU</span><br><span class="line">总 结</span><br></pre></td></tr></table></figure><br>
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<p>本文回顾近几十年发展过程,展望一下未来20年的发展方向。雷军说,站在风口,猪都能飞。既然大家都是猪,就要找好风口。</p>
</blockquote><p>国家发改委首次明确「新基建」范围,具体涵盖</p><ol>
<li>5G 基建</li>
<li>工业互联网</li>
<li>人工智能</li>
<li>大数据中心</li>
<li>高速公路轨道交通</li>
<li>新能源汽车充电桩</li>
<li>特高压</li>
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<p>平衡业务策略与模型之间的关系。策略能力有限,模型也“没有银弹”。</p>
</blockquote><p>最近刚好在研究策略和多样性的关系,就帮DataFunTalk社区又整理了一篇稿子,策略本身很乏味,但是策略可以解决很多模型解决不了的问题。<br>为什么会这样呢?主要还是模型太笨了,由于模型不能完全动态的掌握用户的心理变化,兴趣变化,所以一般的推荐模型都是对于用户的基于历史的点击率CTR预估,可以简化为分类或者回归模型。可是实际上,远远没有这么简单。我觉得最接近”智能化的推荐“的算法就应该是强化学习,算法推出一个item就是一次尝试,用户的行为会及时让系统收到反馈,无论是”多样性“,”准确性“,”兴趣偏好“,”负反馈“,统统都会被融合到Agent的激励里。之后有空了希望进一步调研强化学习在推荐的瓶颈。<br>以下为分享内容。</p>
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<a href="/2020/MSBD5004-Mathematical-Methods-for-Data-Analysis-Homework-5/" class="post-title-link" itemprop="url">MSBD5004 Mathematical Methods for Data Analysis Homework 5</a>
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<p>5004完结撒花🌸🌸ヽ(°▽°)ノ🌸🌸.</p>
</blockquote><h1 id="Q1"><a href="#Q1" class="headerlink" title="Q1"></a>Q1</h1><p><img alt data-src="https://liyuanimage.oss-cn-beijing.aliyuncs.com/img/20200509220809.png?x-oss-process=image/auto-orient,1/quality,q_90/format,jpg"></p><script type="math/tex; mode=display">
\begin{aligned}
&if \ n=0,\ c_0 =\int_{-\frac{1}{2}}^{\frac{1}{2}} t \exp (0) d t=0\\
&if \ n\not=0,\ c_n =\int_{-\frac{1}{2}}^{\frac{1}{2}} t \exp (-2 i \pi k t) d t=\frac{i(\pi n \cos (\pi k)-\sin (\pi n))}{2 \pi^{2} n^{2}}=\frac{(-1)^ni}{2\pi n}\\
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<li>Subword Models 解决OOV问题</li>
</ul><p>OOV: out-of-vocabulary即单词不在词汇库里的情况<br>之前的模型都是基于单词层面的,但是对于一个新单词“asd”没有解决方案。一个朴素的想法就是“拆分”,把长单词拆成几个单词的组合。相对于中文来说,就是一个新的词组去按照每个字的意思去理解单词的意思。<br>下面是解决方案。</p>
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<p>这一节主要讲cnn在nlp中的应用,然后讲了几篇paper的思路。站在现在看CNN其实对于nlp并没有爆发性的突破,但是CNN也算是提取局部特征的思路,可以学习2017年以前那些人的尝试过程,也蛮有意思吧。</p>
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<p>这次作业感觉有点简单啊。🤧 最后一题凭感觉写的,不知道对不对,如果有问题请在留言板告诉我。<br>希望大家留言板支持一下博主,打公式真的好累。☹️ 给个例子让你们参考一下。<br>
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<p>这一节主要在介绍数据集和task。对于模型没太深究。</p>