基于Spark的协同过滤音乐推荐系统源码：利用SpringBoot和Vue框架及ALS模型进行个性化音乐推荐的技术实践,基于SpringBoot与Vue框架的ALS协同过滤音乐推荐系统源码：融合用户行
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基于Spark的协同过滤音乐推荐系统源码：利用SpringBoot和Vue框架及ALS模型进行个性化音乐推荐的技术实践,基于SpringBoot与Vue框架的ALS协同过滤音乐推荐系统源码：融合用户行为与近一年听歌日志及收藏记录的个性化推荐技术解析,spark音乐推荐系统源码 协同过滤音乐推荐技术栈:springboot vue 协同过滤算法算法：als基于模型协同过滤用户行为：近一年听歌日志与收藏记录，听一次歌算1分，收藏算5分,spark音乐推荐系统源码; 协同过滤音乐推荐; 技术栈; springboot; vue; ALS协同过滤算法; 用户行为; 听歌日志; 收藏记录,Spark音乐推荐系统源码：基于SpringBoot与Vue的ALS协同过滤算法实现 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90401618/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90401618/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">基于<span class="_ _0"> </span></span>Spark<span class="_ _1"> </span><span class="ff2">音乐推荐系统的设计与实现</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">一<span class="ff3">、</span>引言</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">随着互联网的快速发展<span class="ff4">，</span>音乐平台已成为人们生活中不可或缺的一部分<span class="ff3">。</span>为了满足用户对个性化音乐</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">的需求<span class="ff4">，</span>我们设计并实现了一个基于<span class="_ _0"> </span><span class="ff1">Spark<span class="_ _1"> </span></span>的音乐推荐系统<span class="ff3">。</span>本系统主要利用协同过滤算法<span class="ff4">（</span>尤其是</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">基于模型的协同过滤算法<span class="ff1">——ALS<span class="ff4">），</span></span>通过<span class="_ _0"> </span><span class="ff1">SpringBoot<span class="_ _1"> </span></span>和<span class="_ _0"> </span><span class="ff1">Vue<span class="_ _1"> </span></span>的技术栈构建了一个后端服务与前端</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">交互的推荐系统<span class="ff3">。</span></div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">二<span class="ff3">、</span>系统架构设计</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff2">技术栈<span class="ff4">：</span></span></div><div class="t m0 x2 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">-<span class="_ _2"> </span><span class="ff2">后端<span class="ff4">：</span></span>SpringBoot<span class="_ _1"> </span><span class="ff2">框架<span class="ff4">，</span>用于构建后端服务<span class="ff4">，</span>处理业务逻辑<span class="ff3">。</span></span></div><div class="t m0 x2 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">-<span class="_ _2"> </span><span class="ff2">前端<span class="ff4">：</span></span>Vue<span class="_ _1"> </span><span class="ff2">框架<span class="ff4">，</span>用于构建用户界面<span class="ff4">，</span>与后端进行交互<span class="ff3">。</span></span></div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">2. <span class="ff2">数据源<span class="ff4">：</span>近一年听歌日志与收藏记录<span class="ff4">，</span>包括用户的听歌行为和收藏行为<span class="ff3">。</span>其中<span class="ff4">，</span>听一次歌算<span class="_ _0"> </span></span>1<span class="_ _1"> </span><span class="ff2">分</span></div><div class="t m0 x1 h2 yc ff4 fs0 fc0 sc0 ls0 ws0">，<span class="ff2">收藏算<span class="_ _0"> </span><span class="ff1">5<span class="_ _1"> </span></span>分<span class="ff3">。</span>这些数据将作为我们推荐算法的输入<span class="ff3">。</span></span></div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">三<span class="ff3">、</span>协同过滤音乐推荐算法<span class="ff1">——ALS<span class="_ _1"> </span></span>模型</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">ALS<span class="ff4">（</span>Alternating Least Squares<span class="ff4">）<span class="ff2">是一种基于模型的协同过滤算法</span>，<span class="ff2">它通过分析用户的行为</span></span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">数据来预测用户对未听过歌曲的喜好程度<span class="ff3">。</span>在本系统中<span class="ff4">，</span>我们将使用<span class="_ _0"> </span><span class="ff1">ALS<span class="_ _1"> </span></span>算法来计算用户与歌曲之间</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">的相似度<span class="ff4">，</span>从而为用户推荐他们可能喜欢的歌曲<span class="ff3">。</span></div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">四<span class="ff3">、</span>数据处理与模型训练</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff2">数据预处理<span class="ff4">：</span>对近一年听歌日志与收藏记录进行清洗<span class="ff3">、</span>格式化<span class="ff4">，</span>将用户行为数据转化为适合模型</span></div><div class="t m0 x3 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">训练的格式<span class="ff3">。</span></div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff2">模型训练<span class="ff4">：</span>利用<span class="_ _0"> </span></span>Spark<span class="_ _1"> </span><span class="ff2">的机器学习库对数据进行训练<span class="ff4">，</span>使用<span class="_ _0"> </span></span>ALS<span class="_ _1"> </span><span class="ff2">算法构建用户</span>-<span class="ff2">歌曲的相似度</span></div><div class="t m0 x3 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">模型<span class="ff3">。</span></div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">五<span class="ff3">、</span>用户行为分析与权重计算</div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">在协同过滤算法中<span class="ff4">，</span>不同的用户行为应具有不同的权重<span class="ff3">。</span>本系统根据用户的听歌日志与收藏记录来计</div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">算权重<span class="ff3">。</span>听一次歌算<span class="_ _0"> </span><span class="ff1">1<span class="_ _1"> </span></span>分<span class="ff4">，</span>反映出用户对歌曲的普通兴趣<span class="ff4">；</span>而收藏算<span class="_ _0"> </span><span class="ff1">5<span class="_ _1"> </span></span>分<span class="ff4">，</span>则反映出用户对歌曲的强</div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">烈喜好<span class="ff3">。</span>系统将根据这些行为和对应的权重来预测用户对未听过歌曲的喜好程度<span class="ff3">。</span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">六<span class="ff3">、</span>系统实现与部署</div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>