ExpScore: Learning Metrics for Recommendation Explanation

Wen, Bingbing; Feng, Yunhe; Zhang, Yongfeng; Shah, Chirag

doi:10.1145/3485447.3512269

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Explainability Evaluation For further analysis, we assess the quality of generated explanations with several newlyadopted metrics (Wen et al 2022), including Relevance, Polarity, Subjectivity, and Grammar Correctness. For Relevance, we invoke Sentence-BERT 5 to obtain embeddings, and compute the cosine similarity between generated explanations and gold standard references.…”

Section: Resultsmentioning

confidence: 99%

Disentangled CVAEs with Contrastive Learning for Explainable Recommendation

Wang

Cai

Melo

et al. 2023

AAAI

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Modern recommender systems are increasingly expected to provide informative explanations that enable users to understand the reason for particular recommendations. However, previous methods struggle to interpret the input IDs of user--item pairs in real-world datasets, failing to extract adequate characteristics for controllable generation. To address this issue, we propose disentangled conditional variational autoencoders (CVAEs) for explainable recommendation, which leverage disentangled latent preference factors and guide the explanation generation with the refined condition of CVAEs via a self-regularization contrastive learning loss. Extensive experiments demonstrate that our method generates high-quality explanations and achieves new state-of-the-art results in diverse domains.

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Section: Resultsmentioning

confidence: 99%

Disentangled CVAEs with Contrastive Learning for Explainable Recommendation

Wang

Cai

Melo

et al. 2023

AAAI

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A Brief Survey of Offline Explainability Metrics for Conversational Recommender Systems

May,

Poudel

2023

2023 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

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Personalized Prompt Learning for Explainable Recommendation

Zhang

Chen

2023

ACM Trans. Inf. Syst.

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Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system’s ease of use, and gain users’ trust. A typical approach to realize it is natural language generation. However, previous works mostly adopt recurrent neural networks to meet the ends, leaving the potentially more effective pre-trained Transformer models under-explored. In fact, user and item IDs, as important identifiers in recommender systems, are inherently in different semantic space as words that pre-trained models were already trained on. Thus, how to effectively fuse IDs into such models becomes a critical issue. Inspired by recent advancement in prompt learning, we come up with two solutions: find alternative words to represent IDs (called discrete prompt learning) and directly input ID vectors to a pre-trained model (termed continuous prompt learning). In the latter case, ID vectors are randomly initialized but the model is trained in advance on large corpora, so they are actually in different learning stages. To bridge the gap, we further propose two training strategies: sequential tuning and recommendation as regularization. Extensive experiments show that our continuous prompt learning approach equipped with the training strategies consistently outperforms strong baselines on three datasets of explainable recommendation.

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ExpScore: Learning Metrics for Recommendation Explanation

Cited by 4 publications

References 22 publications

Disentangled CVAEs with Contrastive Learning for Explainable Recommendation

Disentangled CVAEs with Contrastive Learning for Explainable Recommendation

A Brief Survey of Offline Explainability Metrics for Conversational Recommender Systems

Personalized Prompt Learning for Explainable Recommendation

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