Personalized Prompt Learning for Explainable Recommendation

TL;DR

This paper introduces a novel prompt learning approach for explainable recommendation systems that effectively incorporates user and item IDs into pre-trained Transformer models, improving explanation quality.

Contribution

It proposes continuous prompt learning with new training strategies to better fuse IDs into pre-trained models, enhancing explainable recommendation performance.

Findings

Continuous prompt learning outperforms baselines on three datasets.

Training strategies improve ID integration and explanation quality.

Approach leverages pre-trained Transformers for better explanations.

Abstract

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.