User-Centric Path Reasoning towards Explainable Recommendation

Tai, Chang-You; Huang, Liang-Ying; Huang, Chien-Kun; Ku, Lun-Wei

doi:10.1145/3404835.3462847

Cited by 21 publications

(9 citation statements)

References 29 publications

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“…UCPR [32] (original source code: https://github.com/johnnyjana730/UCPR/) introduces a multi-view structure leveraging not only local sequence reasoning information, but also a view of the user's demand portfolio. The user demand portfolio, built in a pre-processing phase and updated via a multi-step refocusing, makes the path selection process adaptive and effective.…”

Section: Methods Replicationmentioning

confidence: 99%

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

Balloccu¹,

Boratto²,

Christian³

et al. 2023

Preprint

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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 knowledgeaware 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: https://github.com/giacoballoccu/rep-path-reasoning-recsys.

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Section: Methods Replicationmentioning

confidence: 99%

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

Balloccu¹,

Boratto²,

Christian³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For matching or ranking-based explanation (KG path, features, etc. ), the common evaluation metrics are NDCG, Precision, recall and coverage [171,235,262,263,316]. For the evaluation of counterfactual explanation, common metrics are Average Treatment Effect (ATE), replacement, Probablity of Sufficiency (PS) and Probability of Necessity (PN) [56,147,265,266,275].…”

Section: Evaluation Of Explanationsmentioning

confidence: 99%

A Survey on Trustworthy Recommender Systems

Ge¹,

Liu²,

Fu³

et al. 2022

Preprint

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Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired counter-effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization, just to name a few. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. In this survey, we will introduce techniques related to trustworthy and responsible recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation, robustness in recommendation, user controllable recommendation, as well as the relationship between these different perspectives in terms of trustworthy and responsible recommendation. Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research directions on trustworthy recommendation.

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“…This path is selected by the model as the most representative for the recommended product p among the set of all the predicted 𝑘-hop paths L 𝑘 𝑢,𝑝 between user 𝑢 and a not yet interacted product p. Our addressed task, named as Knowledge Graph Reasoning for Explainable Recommendation (KGRE-Rec) consists of two sub-tasks; i) making recommendations for each user and ii) generating a path sequence as the explanation of each recommended product. Path-guided approaches solve the KGRE-Rec problem as a unified task [7,30,36,41,50,51]. Autoregressive Path Generation for Explainable Recommendation.…”

Section: Preliminariesmentioning

confidence: 99%

“…Furthermore, annotating relevant path types manually might be time-consuming and impractical in real-world scenarios. To address these issues, modern methods perform the recommendation and path selection steps jointly, through path reasoning [7,30,36,41,50,51]. They rely on reinforcement learning (RL), with agents optimised to identify paths from users to unseen yet relevant products in the KG.…”

Section: Introductionmentioning

confidence: 99%

Hands on Explainable Recommender Systems with Knowledge Graphs

Balloccu

Boratto

Fenu

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

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Path reasoning methods over knowledge graphs have gained popularity for their potential to improve transparency in recommender systems. However, the resulting models still rely on pre-trained knowledge graph embeddings, fail to fully exploit the interdependence between entities and relations in the KG for recommendation, and may generate inaccurate explanations. In this paper, we introduce PEARLM, a novel approach that efficiently captures user behaviour and product-side knowledge through language modelling. With our approach, knowledge graph embeddings are directly learned from paths over the KG by the language model, which also unifies entities and relations in the same optimisation space. Constraints on the sequence decoding additionally guarantee path faithfulness with respect to the KG. Experiments on two datasets show the effectiveness of our approach compared to state-of-the-art baselines. Source code and datasets: AVAILABLE AFTER GETTING ACCEPTED. CCS CONCEPTS• Information systems → Recommender systems; • Applied computing → Law, social and behavioral sciences.

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User-Centric Path Reasoning towards Explainable Recommendation

Cited by 21 publications

References 29 publications

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

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

A Survey on Trustworthy Recommender Systems

Hands on Explainable Recommender Systems with Knowledge Graphs

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