Towards Explainable Conversational Recommendation

Chen, Zhongxia; Wang, Xiting; Xie, Xing; Parsana, Mehul; Soni, Akshay; Ao, Xiang; Chen, Enhong

doi:10.24963/ijcai.2020/414

Cited by 46 publications

(30 citation statements)

References 15 publications

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“…Controllability addresses the interactivity of explanations, which is applicable to e.g. conversational explanation methods [47,174], interactive interfaces [218,255], human-in-the-loop explanation learning methods (e.g. as [62]) or methods that enable the user to correct explanations [274].…”

Section: Functionally Evaluating Controllabilitymentioning

confidence: 99%

See 1 more Smart Citation

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI

Nauta¹,

Trienes²,

Pathak³

et al. 2022

Preprint

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The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes, also raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often presented as a subjectively validated binary property, we consider it a multi-faceted concept. We identify 12 conceptual properties, such as Compactness and Correctness, that should be evaluated for comprehensively assessing the quality of an explanation. Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practice of more than 300 papers published in the last 7 years at major AI and ML conferences that introduce an XAI method. We find that 1 in 3 papers evaluate exclusively with anecdotal evidence, and 1 in 5 papers evaluate with users. We also contribute to the call for objective, quantifiable evaluation methods by presenting an extensive overview of quantitative XAI evaluation methods. This systematic collection of evaluation methods provides researchers and practitioners with concrete tools to thoroughly validate, benchmark and compare new and existing XAI methods. This also opens up opportunities to include quantitative metrics as optimization criteria during model training in order to optimize for accuracy and interpretability simultaneously.

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Section: Functionally Evaluating Controllabilitymentioning

confidence: 99%

“…Although users are involved in this evaluation method, it is not a standard user study since the user is seen as a system component: the XAI methods use optimization criteria that require humans-in-the-loop for optimal output. Additionally, Chen et al [47] define the "Concept-level feedback Satisfaction…”

Section: Functionally Evaluating Controllabilitymentioning

confidence: 99%

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI

Nauta¹,

Trienes²,

Pathak³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Explainable AI is, in general, an extremely active and critical area of study (Adadi and Berrada, 2018). In a conversational recommendation setting, explanations have recently received attention as well, for example, see (Chen et al, 2020b;Balog and Radlinski, 2020).…”

Section: Metrics For End-to-end Evaluationmentioning

confidence: 99%

Conversational Information Seeking

Zamani¹,

Trippas²,

Dalton³

et al. 2022

Preprint

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Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. This monograph provides a thorough overview of CIS definitions, applications, interactions, interfaces, design, implementation, and evaluation. This monograph views CIS applications as including conversational search, conversational question answering, and conversational recommendation. Our aim is to provide an overview of past research related to CIS, introduce the current state-of-the-art in CIS, highlight the challenges still being faced in the community. and suggest future directions.

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“…conduct multi-goal planning to make a proactive conversational recommendation over multi-type dialogues. A multi-view method is proposed in Chen et al (2020b) for the explainable conversational recommendation. The work of Pecune et al (2020) builds a socially aware CR system engaging its users through a rapportbuilding dialogue to improve users' perception.…”

Section: Related Workmentioning

confidence: 99%

RevCore: Review-Augmented Conversational Recommendation

Yu¹,

Bao²,

Yan³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items. Incorporating external information (e.g., reviews) is a potential solution to alleviate this problem. Given that reviews often provide a rich and detailed user experience on different interests, they are potential ideal resources for providing high-quality recommendations within an informative conversation. In this paper, we design a novel end-to-end framework, namely, Review-augmented Conversational Recommender (RevCore), where reviews are seamlessly incorporated to enrich item information and assist in generating both coherent and informative responses. In detail, we extract sentiment-consistent reviews, perform review-enriched and entity-based recommendations for item suggestions, as well as use a review-attentive encoder-decoder for response generation. Experimental results demonstrate the superiority of our approach in yielding better performance on both recommendation and conversation responding. 1

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Towards Explainable Conversational Recommendation

Cited by 46 publications

References 15 publications

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI

Conversational Information Seeking

RevCore: Review-Augmented Conversational Recommendation

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