Intelligence Graph

TL;DR

The paper introduces the intelligence graph (iGraph), a unified framework combining neural, probabilistic, and logical components to enhance recommendation systems, demonstrating superior performance over existing methods.

Contribution

It proposes the innovative iGraph framework that integrates neural, probabilistic, and logical reasoning for more powerful intelligence architectures.

Findings

Outperforms state-of-the-art baselines in recommendation tasks

Successfully integrates neural, probabilistic, and logical components

Verifies effectiveness through experimental results

Abstract

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.