Personalized Transformer for Explainable Recommendation

Li, Lei; Zhang, Yongfeng; Chen, Li

doi:10.18653/v1/2021.acl-long.383

Cited by 73 publications

(58 citation statements)

References 30 publications

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“…There exist various types of explanation style, such as pre-defined templates [24,49,60], item features [20,54], ranked text [5,12,27], image visualizations [10], knowledge graph paths [1,18,55,56], and reasoning rules [7,46,63], but in this work we focus on generating natural language explanations. Previous works [6,13,26,57] mostly rely on RNN, e.g., LSTM [22] and GRU [14], or unpretrained Transformer [29], leaving the potentially more effective pre-trained models under-explored, which motivates this work.…”

Section: Explainable Recommendationmentioning

confidence: 99%

“…Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation [6,26,29], review summarization [23], and dialog systems [59,61]. In these tasks, user and item IDs are important identifiers for personalization.…”

Section: Personalized Natural Language Generationmentioning

confidence: 99%

“…Meanwhile, explanations that justify why these recommendations are made are becoming more and more important, as they can help users make better and faster decisions, increase the system's ease of use, and gain their trust in the system [50,58]. There is a variety of explanation style, such as pre-defined templates [24,49,60], highlighted image regions [10] and automatically generated sentences [6,26,29]. The last type has gained increasing attention recently, mainly due to the availability of textual data on online commercial platforms, such as Amazon and Yelp, which encourage users to express their opinions by writing reviews (see Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it would also be challenging to incorporate into them user and item IDs, which are indispensable in recommender systems but are in very different semantic space as words that these models were trained on. No wonder most previous works [6,13,26,48,57] adopt RNN, such as Long Short-Term Memory (LSTM) [22] and Gated Recurrent Unit (GRU) [14], or small unpretrained Transformer [29] for explanation generation. This, however, makes the more effective pre-trained models less explored.…”

Section: Introductionmentioning

confidence: 99%

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Personalized Prompt Learning for Explainable Recommendation

Li¹,

Zhang²,

Chen³

2022

Preprint

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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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Section: Explainable Recommendationmentioning

confidence: 99%

Section: Personalized Natural Language Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Personalized Prompt Learning for Explainable Recommendation

Li¹,

Zhang²,

Chen³

2022

Preprint

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“…Explainable AI has been an important topic in recommender systems [5,6,13,36,41,46,47], natural language processing [8,16,20] and computer vision [7,10,15,25,38]. To improve the transparency of deep neural networks, many explanation techniques have been proposed in recent years.…”

Section: Related Work 21 Explainability In Deep Learning and Aimentioning

confidence: 99%

Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning

Tan,

Geng,

et al. 2022

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Structural data well exists in Web applications, such as social networks in social media, citation networks in academic websites, and threads data in online forums. Due to the complex topology, it is difficult to process and make use of the rich information within such data. Graph Neural Networks (GNNs) have shown great advantages on learning representations for structural data. However, the non-transparency of the deep learning models makes it non-trivial to explain and interpret the predictions made by GNNs. Meanwhile, it is also a big challenge to evaluate the GNN explanations, since in many cases, the ground-truth explanations are unavailable.In this paper, we take insights of Counterfactual and Factual (CF 2 ) reasoning from causal inference theory, to solve both the learning and evaluation problems in explainable GNNs. For generating explanations, we propose a model-agnostic framework by formulating an optimization problem based on both of the two casual perspectives. This distinguishes CF 2 from previous explainable GNNs that only consider one of them. Another contribution of the work is the evaluation of GNN explanations. For quantitatively evaluating the generated explanations without the requirement of ground-truth, we design metrics based on Counterfactual and Factual reasoning to evaluate the necessity and sufficiency of the explanations. Experiments show that no matter ground-truth explanations are available or not, CF 2 generates better explanations than previous state-of-the-art methods on real-world datasets. Moreover, the statistic analysis justifies the correlation between the performance on ground-truth evaluation and our proposed metrics. CCS CONCEPTS• Computing methodologies → Machine learning; • Mathematics of computing → Computing most probable explanation; Graph algorithms.

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