An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information

Shimizu, Ryotaro; Matsutani, Megumi; Goto, Masayuki

doi:10.1016/j.knosys.2021.107970

Cited by 43 publications

(5 citation statements)

References 19 publications

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“…These entities were sorted based on the information they captured from the context, and explanations were generated using the triples. Shimizu et al (2022) introduced a knowledge graph attention network that used side information of items to make recommendations and generate explanations. Geng et al (2022) trained a language model on the paths of a knowledge graph consisting of entities and edges, which were based on user actions and item features as well as the relationships between them.…”

Section: Related Work In Explainable Recommendationsmentioning

confidence: 99%

Explaining Recommendation System Using Counterfactual Textual Explanations

Ranjbar¹,

Momtazi²,

Homayounpour³

2023

Preprint

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Currently, there is a significant amount of research being conducted in the field of artificial intelligence to improve the explainability and interpretability of deep learning models. It is found that if end-users understand the reason for the production of some output, it is easier to trust the system. Recommender systems are one example of systems that great efforts have been conducted to make their output more explainable. One method for producing a more explainable output is using counterfactual reasoning, which involves altering minimal features to generate a counterfactual item that results in changing the output of the system. This process allows the identification of input features that have a significant impact on the desired output, leading to effective explanations. In this paper, we present a method for generating counterfactual explanations for both tabular and textual features. We evaluated the performance of our proposed method on three real-world datasets and demonstrated a +5% improvement on finding effective features (based on model-based measures) compared to the baseline method.

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Section: Related Work In Explainable Recommendationsmentioning

confidence: 99%

Explaining Recommendation System Using Counterfactual Textual Explanations

Ranjbar¹,

Momtazi²,

Homayounpour³

2023

Preprint

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“…The attenuated attention mechanism allows assigning different weights in different relation paths and acquires the information from the neighborhoods. In [Shimizu et al, 2022], a new approach to explainable recommendation is presented, leveraging an advanced knowledge graph attention network model that takes into account item-specific side information to deliver highly accurate recommendations. The proposed framework enables direct interpretation of the reasoning behind each recommendation by visualizing the factors that contributed to it.…”

Section: Related Workmentioning

confidence: 99%

Joint Representation of Entities and Relations via Graph Attention Networks for Explainable Recommendations

Boughareb,

Seridi-Bouchelaghem,

Beldjoudi

2023

JWE

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The latest advances in Graph Neural Networks (GNNs), have provided important new ideas for solving the Knowledge Graph (KG) representation problem for recommendation purposes. Although GNNs have an effective graph representation capability, the nonlinear transformations over the layers cause a loss of semantic information and make the generated embeddings hard to explain. In this paper, we investigate the potential of large KGs to perform interpretable recommendation using Graph Attention Networks (GATs). Our goal is to fully exploit the semantic information and preserve inherent knowledge ported in relations by jointly learning low-dimensional embeddings for nodes (i.e., entities) and edges (i.e., properties). Specifically, we feed the original data with additional knowledge from the Linked Open Data (LOD) cloud, and apply GATs to generate a vector representation for each node on the graph. Experiments conducted on three real-world datasets for the top-K recommendation task demonstrate the state-of-the-art performance of the system proposed. In addition to improving predictive performance in terms of precision, recall, and diversity, our approach fully exploits the rich structured information provided by KGs to offer explanation for recommendations.

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“…KGAT+ [74] This is an improvement of the KGAT model [73]. KGAT+ addresses the computational complexity of the conventional KGAT model and maintains high accuracy and interpretability.…”

Section: Model Key Contribution/drawbacksmentioning

confidence: 99%

“…In [74], Shimizu et al employed KGAT model [73] to develop an improved modelintrinsic (KGAT+) knowledge-based explainable recommendation framework for realworld services. KGAT+ addresses the computational complexity of the conventional KGAT model and maintains high accuracy and interpretability.…”

Section: Model Key Contribution/drawbacksmentioning

confidence: 99%

A Systematic Review of Deep Knowledge Graph-Based Recommender Systems, with Focus on Explainable Embeddings

Doh

Zhou

Arthur

et al. 2022

Data

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Recommender systems (RS) have been developed to make personalized suggestions and enrich users’ preferences in various online applications to address the information explosion problems. However, traditional recommender-based systems act as black boxes, not presenting the user with insights into the system logic or reasons for recommendations. Recently, generating explainable recommendations with deep knowledge graphs (DKG) has attracted significant attention. DKG is a subset of explainable artificial intelligence (XAI) that utilizes the strengths of deep learning (DL) algorithms to learn, provide high-quality predictions, and complement the weaknesses of knowledge graphs (KGs) in the explainability of recommendations. DKG-based models can provide more meaningful, insightful, and trustworthy justifications for recommended items and alleviate the information explosion problems. Although several studies have been carried out on RS, only a few papers have been published on DKG-based methodologies, and a review in this new research direction is still insufficiently explored. To fill this literature gap, this paper uses a systematic literature review framework to survey the recently published papers from 2018 to 2022 in the landscape of DKG and XAI. We analyze how the methods produced in these papers extract essential information from graph-based representations to improve recommendations’ accuracy, explainability, and reliability. From the perspective of the leveraged knowledge-graph related information and how the knowledge-graph or path embeddings are learned and integrated with the DL methods, we carefully select and classify these published works into four main categories: the Two-stage explainable learning methods, the Joint-stage explainable learning methods, the Path-embedding explainable learning methods, and the Propagation explainable learning methods. We further summarize these works according to the characteristics of the approaches and the recommendation scenarios to facilitate the ease of checking the literature. We finally conclude by discussing some open challenges left for future research in this vibrant field.

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An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information

Cited by 43 publications

References 19 publications

Explaining Recommendation System Using Counterfactual Textual Explanations

Explaining Recommendation System Using Counterfactual Textual Explanations

Joint Representation of Entities and Relations via Graph Attention Networks for Explainable Recommendations

A Systematic Review of Deep Knowledge Graph-Based Recommender Systems, with Focus on Explainable Embeddings

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