NFM

1. 算法介绍

NFM(Neural Factorization Machines)算法是在Embedding的基础上, 对Embedding的结果进行两两对应元素乘积, 然后相加, 得到一个与Embedding同维的向量, 然后输入DNN进一步提取高阶特特交叉. 值得注意的是, NFM也没有放弃一阶特征, 最后将一阶特征与高阶特征组合起来进行预测, 其构架如下:

NFM

1.1 BiInteractionCross层的说明

在实现中, 用Embedding的方式存储Neural Factorization Machine(NFM) - 图2, 调用Embedding的calOutput后, 将Neural Factorization Machine(NFM) - 图3计算后一起输出, 所以一个样本的Embedding output结果为:

model=(\bold{u}_1,\bold{u}_2,\bold{u}_3,\cdots,\bold{u}_k))

BiInteractionCross的计算公式如下:

model\\&=\frac{1}{2}(\sum_i\bold{u}_i)\otimes(\sum_j\bold{u}_j)-\sum_i\bold{u}_i^2\\&=\frac{1}{2}[(\sum_i\bold{u}_i)^2-\sum_i\bold{u}_i^2]\end{array})

用Scala代码实现为:

  1.   val sum1Vector = VFactory.denseDoubleVector(outputDim)
  2.   val sum2Vector = VFactory.denseDoubleVector(outputDim)
  3.   (0 until batchSize).foreach { row =>
  4.       mat.getRow(row).getPartitions.foreach { vectorOuter =>
  5.       sum1Vector.iadd(vectorOuter)
  6.       sum2Vector.iadd(vectorOuter.mul(vectorOuter))
  7.     }
  9.     blasMat.setRow(row, sum1Vector.imul(sum1Vector).isub(sum2Vector).imul(0.5))
  10.     sum1Vector.clear()
  11.     sum2Vector.clear()
  12.   }

1.2 其它层说明

  • SparseInputLayer: 稀疏数据输入层, 对稀疏高维数据做了特别优化, 本质上是一个FCLayer
  • Embedding: 隐式嵌入层, 如果特征非one-hot, 则乘以特征值
  • FCLayer: DNN中最常见的层, 线性变换后接传递函数
  • SumPooling: 将多个输入的数据做element-wise的加和, 要求输入具本相同的shape
  • SimpleLossLayer: 损失层, 可以指定不同的损失函数

1.3 网络构建

  1.   override def buildNetwork(): Unit = {
  2.     val wide = new SparseInputLayer("input", 1, new Identity(),
  3.       JsonUtils.getOptimizerByLayerType(jsonAst, "SparseInputLayer"))
  5.     val embeddingParams = JsonUtils.getLayerParamsByLayerType(jsonAst, "Embedding")
  6.       .asInstanceOf[EmbeddingParams]
  7.     val embedding = new Embedding("embedding", embeddingParams.outputDim, embeddingParams.numFactors,
  8.       embeddingParams.optimizer.build()
  9.     )
  11.     val interactionCross = new BiInteractionCross("BiInteractionCross", embeddingParams.numFactors, embedding)
  12.     val hiddenLayer = JsonUtils.getFCLayer(jsonAst, interactionCross)
  14.     val join = new SumPooling("sumPooling", 1, Array[Layer](wide, hiddenLayer))
  16.     new SimpleLossLayer("simpleLossLayer", join, lossFunc)
  17.   }

2. 运行与性能

2.1 Json配置文件说明

NFM的参数较多, 需要用Json配置文件的方式指定(关于Json配置文件的完整说明请参考Json说明), 一个典型的例子如下:

  1. {
  2.   "data": {
  3.     "format": "dummy",
  4.     "indexrange": 148,
  5.     "numfield": 13,
  6.     "validateratio": 0.1
  7.   },
  8.   "model": {
  9.     "modeltype": "T_FLOAT_SPARSE_LONGKEY",
  10.     "modelsize": 148
  11.   },
  12.   "train": {
  13.     "epoch": 10,
  14.     "numupdateperepoch": 10,
  15.     "lr": 0.01,
  16.     "decay": 0.1
  17.   },
  18.   "default_optimizer": "Momentum",
  19.   "layers": [
  20.     {
  21.       "name": "wide",
  22.       "type": "sparseinputlayer",
  23.       "outputdim": 1,
  24.       "transfunc": "identity"
  25.     },
  26.     {
  27.       "name": "embedding",
  28.       "type": "embedding",
  29.       "numfactors": 8,
  30.       "outputdim": 104,
  31.       "optimizer": {
  32.         "type": "momentum",
  33.         "momentum": 0.9,
  34.         "reg2": 0.01
  35.       }
  36.     },
  37.     {
  38.       "name": "biinteractioncross",
  39.       "type": "BiInteractionCross",
  40.       "outputdim": 8,
  41.       "inputlayer": "embedding"
  42.     },
  43.     {
  44.       "name": "fclayer",
  45.       "type": "FCLayer",
  46.       "outputdims": [
  47.         50,
  48.         50,
  49.         1
  50.       ],
  51.       "transfuncs": [
  52.         "relu",
  53.         "relu",
  54.         "identity"
  55.       ],
  56.       "inputlayer": "biinteractioncross"
  57.     },
  58.     {
  59.       "name": "sumPooling",
  60.       "type": "SumPooling",
  61.       "outputdim": 1,
  62.       "inputlayers": [
  63.         "wide",
  64.         "fclayer"
  65.       ]
  66.     },
  67.     {
  68.       "name": "simplelosslayer",
  69.       "type": "simplelosslayer",
  70.       "lossfunc": "logloss",
  71.       "inputlayer": "sumPooling"
  72.     }
  73.   ]
  74. }

2.2 提交脚本说明

  1. runner="com.tencent.angel.ml.core.graphsubmit.GraphRunner"
  2. modelClass="com.tencent.angel.ml.classification.NeuralFactorizationMachines"
  4. $ANGEL_HOME/bin/angel-submit \
  5.     --angel.job.name DeepFM \
  6.     --action.type train \
  7.     --angel.app.submit.class $runner \
  8.     --ml.model.class.name $modelClass \
  9.     --angel.train.data.path $input_path \
  10.     --angel.workergroup.number $workerNumber \
  11.     --angel.worker.memory.gb $workerMemory  \
  12.     --angel.ps.number $PSNumber \
  13.     --angel.ps.memory.gb $PSMemory \  
  14.     --angel.task.data.storage.level $storageLevel \
  15.     --angel.task.memorystorage.max.gb $taskMemory

对深度学习模型, 其数据, 训练和网络的配置请优先使用Json文件指定.