def augment_inner_product_matrix(factors):
    """ This function transforms a factor matrix such that an angular nearest neighbours search
    will return top related items of the inner product.

    This involves transforming each row by adding one extra dimension as suggested in the paper:
    "Speeding Up the Xbox Recommender System Using a Euclidean Transformation for Inner-Product
    Spaces" https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/XboxInnerProduct.pdf

    Basically this involves transforming each feature vector so that they have the same norm, which
    means the cosine of this transformed vector is proportional to the dot product (if the other
    vector in the cosine has a 0 in the extra dimension). """
    norms = numpy.linalg.norm(factors, axis=1)
    max_norm = norms.max()

    # add an extra dimension so that the norm of each row is the same
    # (max_norm)
    extra_dimension = numpy.sqrt(max_norm ** 2 - norms ** 2)
    return max_norm, numpy.append(factors, extra_dimension.reshape(norms.shape[0], 1), axis=1)
This function transforms a factor matrix such that an angular nearest neighbours search
    will return top related items of the inner product.

    This involves transforming each row by adding one extra dimension as suggested in the paper:
    "Speeding Up the Xbox Recommender System Using a Euclidean Transformation for Inner-Product
    Spaces" https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/XboxInnerProduct.pdf

    Basically this involves transforming each feature vector so that they have the same norm, which
    means the cosine of this transformed vector is proportional to the dot product (if the other
    vector in the cosine has a 0 in the extra dimension).