Model-Agnostic Counterfactual Explanations of Recommendations

Kaffes, Vassilis; Sacharidis, Dimitris; Giannopoulos, Giorgos

doi:10.1145/3450613.3456846

Cited by 23 publications

(22 citation statements)

References 20 publications

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“…On one hand, post-hoc explainable models [18] consider recommendation and explanation generation as two distinct steps. Methods pertaining to this family either pre-compute paths in the knowledge graph and attach them to the recommended products generated by traditional models [5,1] or generate counterfactual explanations [14,13,9,7]. In both cases, the ranked products are optimized for utility and the training process of the recommendation model does not embed any constraint for the selection of accompanying reasoning paths.…”

Section: Introductionmentioning

confidence: 99%

XRecSys: A framework for path reasoning quality in explainable recommendation

et al. 2022

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Section: Introductionmentioning

confidence: 99%

XRecSys: A framework for path reasoning quality in explainable recommendation

et al. 2022

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“…), 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]. Some other evaluation metrics include Perplexity, Mean Explanation Precision (MEP), Mean Explanation Recall (MER) as well as influence [1,187,192,224].…”

Section: Evaluation Of Explanationsmentioning

confidence: 99%

“…• Causal explanations. Recently, there are several works [111,147,266,275] which explore the counterfactual explainable recommender system from the causal perspective. However, finergrained quantification of causal explanations is desired and yet to be explored, such as quantifying the causal treatment effect of the explanations and extracting the causally strongest explanations accordingly.…”

Section: Open Problems and Relationship With Other Trustworthy Perspe...mentioning

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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“…They use the influence function to compute how the predicted score on the explained item might change if some training items were removed. Furthermore, Kaffes et al [34] propose a black-box solution that performs Breadth First Search with heuristics that combine search length and drop of the item rank if the candidate set were considered.…”

Section: Counterfactual Machine Learningmentioning

confidence: 99%

Counterfactually Evaluating Explanations in Recommender Systems

Yao¹,

Wang²,

Li³

2022

Preprint

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Modern recommender systems face an increasing need to explain their recommendations. Despite considerable progress in this area, evaluating the quality of explanations remains a significant challenge for researchers and practitioners. Prior work mainly conducts human study to evaluate explanation quality, which is usually expensive, time-consuming, and prone to human bias. In this paper, we propose an offline evaluation method that can be computed without human involvement. To evaluate an explanation, our method quantifies its counterfactual impact on the recommendation. To validate the effectiveness of our method, we carry out an online user study. We show that, compared to conventional methods, our method can produce evaluation scores more correlated with the real human judgments, and therefore can serve as a better proxy for human evaluation. In addition, we show that explanations with high evaluation scores are considered better by humans. Our findings highlight the promising direction of using the counterfactual approach as one possible way to evaluate recommendation explanations.

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Model-Agnostic Counterfactual Explanations of Recommendations

Cited by 23 publications

References 20 publications

XRecSys: A framework for path reasoning quality in explainable recommendation

XRecSys: A framework for path reasoning quality in explainable recommendation

A Survey on Trustworthy Recommender Systems

Counterfactually Evaluating Explanations in Recommender Systems

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