Personalized Transformer for Explainable Recommendation

2021/05/25 by Lei Li, Yongfeng Zhang, Li, Lei +3 · 9 citations
Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Recommender Systems and Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2105.11601

openalex publication_date 2021/05/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 (PETER), 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 recommendation-explanation 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.

Citations

• On the difficulty of training Recurrent Neural Networks
• A Stochastic Approximation Method
• Long Short-Term Memory

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• Bridging Modality Gaps in e-Commerce Products via Vision-Language Alignment
• FIRE: Faithful Interpretable Recommendation Explanations
• Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation
• Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations
• "This Suits You the Best": Query Focused Comparative Explainable Summarization

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