# Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation

arXiv: https://arxiv.org/abs/2301.05944 · HTML: https://mlsanity.com/paper/2301.05944/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 14 Jan 2023.

## Builds on (most cited first, 21 of 21)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [A Theoretical Analysis of NDCG Type Ranking Measures](https://mlsanity.com/paper/1304.6480/) (2013)
- [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://mlsanity.com/paper/1906.05237/) (2019)
- [Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences](https://mlsanity.com/paper/1902.06236/) (2019)
- [Jointly Learning Explainable Rules for Recommendation with Knowledge Graph](https://mlsanity.com/paper/1903.03714/) (2019)
- [Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation](https://mlsanity.com/paper/1805.03352/) (2018)
- [CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation](https://mlsanity.com/paper/2010.15620/) (2020)
- [Neural Collective Entity Linking](https://mlsanity.com/paper/1811.08603/) (2018)
- [KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems](https://mlsanity.com/paper/1807.11141/) (2018)
- [Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems](https://mlsanity.com/paper/2006.04279/) (2020)
- [Time-aware Path Reasoning on Knowledge Graph for Recommendation](https://mlsanity.com/paper/2108.02634/) (2021)
- [Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations](https://mlsanity.com/paper/2201.08614/) (2022)
- [Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning](https://mlsanity.com/paper/2003.12718/) (2020)
- [Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality](https://mlsanity.com/paper/2209.04954/) (2022)
- [Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations](https://mlsanity.com/paper/2204.11241/) (2022)
- [Ekar: An Explainable Method for Knowledge Aware Recommendation](https://mlsanity.com/paper/1906.09506/) (2019)

## Built on by (most cited first, 2 of 2)

- [How Fair is Your Diffusion Recommender Model?](https://mlsanity.com/paper/2409.04339/) (2024)
- [Fair Augmentation for Graph Collaborative Filtering](https://mlsanity.com/paper/2408.12208/) (2024)

## Abstract (arXiv)

Path reasoning is a notable recommendation approach that models high-order user-product relations, based on a Knowledge Graph (KG). This approach can extract reasoning paths between recommended products and already experienced products and, then, turn such paths into textual explanations for the user. Unfortunately, evaluation protocols in this field appear heterogeneous and limited, making it hard to contextualize the impact of the existing methods. In this paper, we replicated three state-of-the-art relevant path reasoning recommendation methods proposed in top-tier conferences. Under a common evaluation protocol, based on two public data sets and in comparison with other knowledge-aware methods, we then studied the extent to which they meet recommendation utility and beyond objectives, explanation quality, and consumer and provider fairness. Our study provides a picture of the progress in this field, highlighting open issues and future directions. Source code: urlhttps://github.com/giacoballoccu/rep-path-reasoning-recsys.