ELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations

2025-08-27

AI Summary

Existing collaborative filtering methods suffer from limited capability in fine-grained preference modeling and explanation generation, failing to jointly optimize rating prediction accuracy and personalized interpretability. To address this, we propose a multi-task explainable recommendation framework that jointly learns global and aspect-level user–item representations. A personalized attention mechanism is introduced to dynamically weight the importance of different aspects according to individual user preferences. Leveraging T5-small, the model simultaneously optimizes three objectives: overall rating prediction, aspect-level rating prediction, and personalized review generation. Extensive experiments on TripAdvisor and RateBeer demonstrate that our approach significantly outperforms strong baselines—particularly in generating semantically coherent, user-specific, high-quality natural language explanations. To the best of our knowledge, this is the first work to achieve deep joint modeling of precise rating prediction and fine-grained, natural-language-based explanations.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Collaborative filtering drives many successful recommender systems but struggles with fine-grained user-item interactions and explainability. As users increasingly seek transparent recommendations, generating textual explanations through language models has become a critical research area. Existing methods employ either RNNs or Transformers. However, RNN-based approaches fail to leverage the capabilities of pre-trained Transformer models, whereas Transformer-based methods often suffer from suboptimal adaptation and neglect aspect modeling, which is crucial for personalized explanations. We propose ELIXIR (Efficient and LIghtweight model for eXplaIning Recommendations), a multi-task model combining rating prediction with personalized review generation. ELIXIR jointly learns global and aspect-specific representations of users and items, optimizing overall rating, aspect-level ratings, and review generation, with personalized attention to emphasize aspect importance. Based on a T5-small (60M) model, we demonstrate the effectiveness of our aspect-based architecture in guiding text generation in a personalized context, where state-of-the-art approaches exploit much larger models but fail to match user preferences as well. Experimental results on TripAdvisor and RateBeer demonstrate that ELIXIR significantly outperforms strong baseline models, especially in review generation.

Problem

• Addressing fine-grained user-item interaction challenges in collaborative filtering
• Improving explainability of recommendations through personalized text generation
• Overcoming limitations of RNN and Transformer models for aspect modeling

Innovation

• Multi-task model combining rating prediction and review generation
• Joint learning of global and aspect-specific user-item representations
• Personalized attention mechanism emphasizing aspect importance