Inferring Substitutable and Complementary Products with Knowledge-Aware Path Reasoning based on Dynamic Policy Network

TL;DR

This paper introduces a novel knowledge-aware path reasoning model using a dynamic policy network to infer substitutable and complementary products, improving decision-making over knowledge graphs in recommender systems.

Contribution

The paper proposes a new model that models product inference as a Markov Decision Process with a dynamic policy network for explicit reasoning.

Findings

Achieves competitive performance on real-world datasets.

Effectively integrates structured and unstructured knowledge.

Provides explicit reasoning paths for product relationship inference.

Abstract

Inferring the substitutable and complementary products for a given product is an essential and fundamental concern for the recommender system. To achieve this, existing approaches take advantage of the knowledge graphs to learn more evidences for inference, whereas they often suffer from invalid reasoning for lack of elegant decision making strategies. Therefore, we propose a novel Knowledge-Aware Path Reasoning (KAPR) model which leverages the dynamic policy network to make explicit reasoning over knowledge graphs, for inferring the substitutable and complementary relationships. Our contributions can be highlighted as three aspects. Firstly, we model this inference scenario as a Markov Decision Process in order to accomplish a knowledge-aware path reasoning over knowledge graphs. Secondly,we integrate both structured and unstructured knowledge to provide adequate evidences for making accurate decision-making. Thirdly, we evaluate our model on a series of real-world datasets, achieving competitive performance compared with state-of-the-art approaches. Our code is released on https://gitee.com/yangzijing flower/kapr/tree/master.