可解釋型推薦任務旨在接收「使用者」和「項目」的配對，並輸出解釋以說明為什麼推薦某項目給使用者。在自然語言解釋型推薦模型的背景下，一個好的解釋應具有以下特徵：1）多樣性：對於相同的項目，模型應為不同使用者生成個性化的理由；2）事實性：推薦的特徵或內容應與項目事實相關；3）精確性：推薦的特徵應盡可能精確（而不是過於泛化）。雖然如今大部分該任務模型能生成流利且符合語法的句子，但面臨過於泛化或虛構內容的問題。為了解決這一問題，我們引入了來自基於面向的情緒分析（Aspect-based Sentiment Analysis）中“多面向＂（multi-aspect）概念：我們假設餐廳領域中存在有限的多個不同的面向，如“食物品質＂（food quality）或“服務＂(service)，提供更細緻的使用者偏好理解。在本文中，我們提出了一種簡單而有效的提示學習（prompt learning）方法，將面向作為輔助信號以提高模型對不同面向類別詞語的記憶，進而在生成解釋時提及更豐富多樣的面向詞彙。該模型框架稱為多面向提示學習模型（Multi-Aspect Prompt LEarner，簡稱 MAPLE）。我們的貢獻如下：1. MAPLE 提高了生成特徵的多樣性和精確性，我們自行發明的面向解釋性指標（aspect-wise explanability）所計算的數據也支持這個發現。2. MAPLE 生成的解釋在資料檢索領域中的「檢索-閱讀框架」retriever-reader framework）中可以被當作一個品質優秀的查詢生成器（query generator）。通過與潛在個性化檢索模型的比較，我們顯示 MAPLE 更準確地預測了面向關係並且能生成出更準確與多樣的面向詞彙（aspect term），從數據與案例研究都支持這一發現。3. 我們更新了餐廳領域的評論生成資料集，包括更高品質的面向詞語並額外標註了相關的面向類別，增強了該領域的研究。

Explainable Recommendation task is designed to receive a pair of user and item and output explanations to justify why an item is recommended to a user. Many models treat review-generation as a proxy of explainable recommendation. Although they are able to generate fluent and grammatical sentences, they suffer from generality and hallucination issues. We propose a personalized, aspect-controlled model called Multi-Aspect Prompt LEarner (MAPLE), in which it integrates aspect category as another input dimension to facilitate the memorization of fine-grained aspect terms. Experiments on two real-world review datasets in restaurant domain show that MAPLE outperforms the baseline review-generation models in terms of text and feature diversity while maintaining excellent coherence and factual relevance. We further treat MAPLE as a retriever component in the retriever-reader frame-work and employ a Large-Language Model (LLM) as the reader, showing that MAPLE’ explanation along with the LLM’s comprehension ability leads to enriched and personalized explanation as a result. We will release the code and data in this http upon acceptance.

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