Causality Learning: A New Perspective for Interpretable Machine Learning

Xu, Guandong; Duong, Tri Dung; Li, Qian; Liu, Shaowu; Wang, Xianzhi

doi:10.48550/arxiv.2006.16789

Cited by 22 publications

(19 citation statements)

References 35 publications

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“…We explore item-item paths between consecutive items with attention mechanisms for users' sequential behaviour modelling. Future works may include the following directions: 1) generate human-readable explanations for recommendation with NLP techniques, and 2) explore causality [36] to discover more appealing paths for explainablity.…”

Section: Discussionmentioning

confidence: 99%

Temporal Meta-path Guided Explainable Recommendation

Chen

Sun

et al. 2021

Proceedings of the 14th ACM International Conference on Web Search and Data Mining

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Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendation only utilizes static knowledge graph and ignores the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling user's temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling user's sequential interactions within a path or independently and separately of the recommendation mechanism. In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation (TMER), which utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture users' historical item features and path-based context to characterise next purchased item. Extensive evaluations of TMER on three real-world benchmark datasets show state-of-the-art performance compared against recent strong baselines. 1 CCS CONCEPTS• Information systems → Data mining.

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Section: Discussionmentioning

confidence: 99%

Temporal Meta-path Guided Explainable Recommendation

Chen

Sun

et al. 2021

Proceedings of the 14th ACM International Conference on Web Search and Data Mining

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“…All the above experiments have proved the explainability, effectiveness, highperformance of the TMER-RL. Future works may include the following directions: 1) generate human-readable explanations for recommendation with NLP techniques, and 2) explore causality learning [59] to discover more appealing paths for explainablity.…”

Section: Discussionmentioning

confidence: 99%

Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

Li¹,

Chen²,

Li³

et al. 2021

Preprint

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Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling user's temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling user's sequential interactions within a path or independently and separately of the recommendation mechanism. In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture users' historical item features and path-based context to characterize the next purchased item. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.

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“…Causal Inference (CI) [15] is a collection of techniques to discover and quantify cause-effect relations from data. Causal inference techniques have been used in a broad range of domains, including process mining.…”

Section: Causal Inferencementioning

confidence: 99%

Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

Shoush¹,

Dumas²

2021

Preprint

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Prescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, specific interventions consume resources with finite capacity. For example, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant's chances of taking a loan. This intervention requires a certain amount of time from a credit officer, and thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The proposed technique relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on the outcome of the case. These outputs are then used to allocate resources to interventions to maximize a cost function. A preliminary empirical evaluation suggests that the proposed approach produces a higher net gain than a purely predictive (non-causal) baseline.

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Causality Learning: A New Perspective for Interpretable Machine Learning

Cited by 22 publications

References 35 publications

Temporal Meta-path Guided Explainable Recommendation

Temporal Meta-path Guided Explainable Recommendation

Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

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