example/sparse/matrix_factorization/model.py

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import mxnet as mx

def matrix_fact_net(factor_size, num_hidden, max_user, max_item, sparse_embed=True):
    # input
    user = mx.symbol.Variable('user')
    item = mx.symbol.Variable('item')
    score = mx.symbol.Variable('score')
    if sparse_embed:
        # user feature lookup
        user_weight = mx.symbol.Variable('user_weight', stype='row_sparse')
        user = mx.symbol.contrib.SparseEmbedding(data=user, weight=user_weight,
                                                 input_dim=max_user, output_dim=factor_size)
        # item feature lookup
        item_weight = mx.symbol.Variable('item_weight', stype='row_sparse')
        item = mx.symbol.contrib.SparseEmbedding(data=item, weight=item_weight,
                                                 input_dim=max_item, output_dim=factor_size)
    else:
        # user feature lookup
        user = mx.symbol.Embedding(data=user, input_dim=max_user, output_dim=factor_size)
        # item feature lookup
        item = mx.symbol.Embedding(data=item, input_dim=max_item, output_dim=factor_size)
    # non-linear transformation of user features
    user = mx.symbol.Activation(data=user, act_type='relu')
    user = mx.symbol.FullyConnected(data=user, num_hidden=num_hidden)
    # non-linear transformation of item features
    item = mx.symbol.Activation(data=item, act_type='relu')
    item = mx.symbol.FullyConnected(data=item, num_hidden=num_hidden)
    # predict by the inner product, which is elementwise product and then sum
    pred = user * item
    pred = mx.symbol.sum(data=pred, axis = 1)
    pred = mx.symbol.Flatten(data=pred)
    # loss layer
    pred = mx.symbol.LinearRegressionOutput(data=pred, label=score)
    return pred