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byTAGS 【本地】通过标签进行推荐 1.将原数据转换为电影-标签频数表 2.计算电影-标签的TF-IDF表 3.将原数据转换为人-电影评分表 4.得到人-标签的兴趣表 5.通过标签向量的余弦相似度计算人-电影的兴趣表 6.交叉验证得到推荐准确度为 【spark】 协同过滤： itemCF userCF als求解的潜语义模型 LFM 交叉验证模块，5折 评价方式： 1.打分的MSE 2.在用户打 所有用户对所有未看过电影的预测评分排序 对用户抽样人工看结果
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