CAESAR: context-aware explanation based on supervised attention for service recommendations

Li, Lei; Chen, Li; Dong, Ruihai

doi:10.1007/s10844-020-00631-8

Cited by 22 publications

(10 citation statements)

References 46 publications

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“…The former investigates how people perceive different styles of explanation [8,9,19], while the latter provides explanations by designing new explainable recommendation algorithms, to which our work is more related. 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%

“…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%

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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: Introductionmentioning

confidence: 99%

Personalized Prompt Learning for Explainable Recommendation

Li¹,

Zhang²,

Chen³

2022

Preprint

Self Cite

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show abstract

“…The former (Gedikli et al, 2014;Chen and Wang, 2017;Chen et al, 2019b) investigates how people perceive different styles of explanations, while the latter provides explanations by designing new explainable recommendation algorithms, to which our work is more related. There exist various types of explanation styles, such as pre-defined templates (Zhang et al, 2014;Li et al, 2020a), ranked sentences (Chen et al, 2019d;, image visualizations (Chen et al, 2019c), knowledge graph paths Xian et al, 2019;, reasoning rules (Shi et al, 2020;Zhu et al, 2021), etc., among which, recently, generated natural language explanations (Ni et al, 2019;Li et al, 2020c) have received much attention, mainly owing to the advancement of natural language generation technology and the availability of textual data on recommendation platforms such as e-commerce. However, previous works mostly rely on recurrent neural networks (RNN), e.g., LSTM (Hochreiter and Schmidhuber, 1997) and GRU (Cho et al, 2014), leaving the potentially more effective Transformer under-explored, which motivates this work.…”

Section: Related Workmentioning

confidence: 99%

Personalized Transformer for Explainable Recommendation

Li¹,

Zhang²,

Chen³

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item IDs are important identifiers for personalization. Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words. To address this problem, we present a PErsonalized Transformer for Explainable Recommendation (PE-TER 1 ), on which we design a simple and effective learning objective that utilizes the IDs to predict the words in the target explanation, so as to endow the IDs with linguistic meanings and to achieve personalized Transformer. Besides generating explanations, PETER can also make recommendations, which makes it a unified model for the whole recommendationexplanation pipeline. Extensive experiments show that our small unpretrained model outperforms fine-tuned BERT on the generation task, in terms of both effectiveness and efficiency, which highlights the importance and the nice utility of our design.

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“…Compared with traditional RS algorithms, e.g., collaborative filtering [16,17], which aim to tackle the information overload problem for users, explainable RS can further improve users' satisfaction and overall experience [18] by helping them better understand recommended items. However, as explanations can take various forms, such as pre-defined template [9,24], generated text [3,10] and path on knowledge graph [6,21], it is difficult to evaluate the explanations produced by different methods.…”

Section: Usersmentioning

confidence: 99%

EXTRA: Explanation Ranking Datasets for Explainable Recommendation

Zhang

Chen

2021

Preprint

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Recently, research on explainable recommender systems (RS) has drawn much attention from both academia and industry, resulting in a variety of explainable models. As a consequence, their evaluation approaches vary from model to model, which makes it quite difficult to compare the explainability of different models. To achieve a standard way of evaluating recommendation explanations, we provide three benchmark datasets for EXplanaTion RAnking (denoted as EXTRA), on which explainability can be measured by rankingoriented metrics. Constructing such datasets, however, presents great challenges. First, user-item-explanation interactions are rare in existing RS, so how to find alternatives becomes a challenge. Our solution is to identify nearly duplicate or even identical sentences from user reviews. This idea then leads to the second challenge, i.e., how to efficiently categorize the sentences in a dataset into different groups, since it has quadratic runtime complexity to estimate the similarity between any two sentences. To mitigate this issue, we provide a more efficient method based on Locality Sensitive Hashing (LSH) that can detect near-duplicates in sub-linear time for a given query. Moreover, we plan to make our code publicly available, to allow other researchers create their own datasets. CCS CONCEPTS• Information systems → Recommender systems; Learning to rank.

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CAESAR: context-aware explanation based on supervised attention for service recommendations

Cited by 22 publications

References 46 publications

Personalized Prompt Learning for Explainable Recommendation

Personalized Prompt Learning for Explainable Recommendation

Personalized Transformer for Explainable Recommendation

EXTRA: Explanation Ranking Datasets for Explainable Recommendation

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