Intelligence Graph
  In fact, there exist three genres of intelligence architectures: logics (e.g.
\textit{Random Forest, A$^*$ Searching}), neurons (e.g. \textit{CNN, LSTM}) and
probabilities (e.g. \textit{Naive Bayes, HMM}), all of which are incompatible
to each other. However, to construct powerful intelligence systems with various
methods, we propose the intelligence graph (short as \textbf{\textit{iGraph}}),
which is composed by both of neural and probabilistic graph, under the
framework of forward-backward propagation. By the paradigm of iGraph, we design
a recommendation model with semantic principle. First, the probabilistic
distributions of categories are generated from the embedding representations of
users/items, in the manner of neurons. Second, the probabilistic graph infers
the distributions of features, in the manner of probabilities. Last, for the
recommendation diversity, we perform an expectation computation then conduct a
logic judgment, in the manner of logics. Experimentally, we beat the
state-of-the-art baselines and verify our conclusions.
