可解釋推薦系統旨在為使用者提供推薦，並提供這些推薦的解釋。透過闡明每個推薦背後的邏輯，可解釋系統可以增強使用者對推薦系統的信任、滿意度和決策能力。過往文獻中提出了不同的可解釋演算法，且每種演算法都執行在不同的資料集。這使得比較不同的解釋變得困難。此外，重現文獻中的結果也具挑戰性。
本研究專注於為健康推薦系統產生和比較解釋。我們運用四種推薦系統在Amazon.com產品評論的單一資料集上。這四種方法分別為基於使用者的協同過濾（User-based collaborative filtering）、基於項目的協同過濾（Item-based collaborative filtering）、基於內容之推薦系統（Content-based recommender system）及基於圖像之推薦系統（Graph-based recommender system）。對於每種推薦系統，我們評估了四種解釋類型的適用性和影響：基於使用者之解釋、基於項目之解釋、基於特徵之解釋、反事實解釋。我們的目標為比較不同的解釋在健康推薦系統中的優點與缺點。我們的結果能幫助企業、使用者及研究人員更好地理解不同推薦系統之解釋的適用性。

Explainable recommender systems aim to provide users with recommendations while also offering explanations for those recommendations. By clarifying the reasoning behind each recommendation, explainable systems enhance user trust, satisfaction, and decision-making. Different explanation algorithms have been proposed in the literature, each illustrating the method on a different dataset. This makes it difficult to compare the different explanations. Furthermore, reproducing the results reported in the literature proves challenging. In this research, we focus on generating and comparing various explanation types for recommender systems in the context of health lifestyle applications. We apply four recommendation techniques - user-based collaborative filtering, item-based collaborative filtering, content-based recommender system, and graph-based recommender system - to a single dataset consisting of product reviews on Amazon.com. For each technique, we examine the applicability and impact of four explanation types: user-based, item-based, feature-based, and counterfactual explanations. Our goal is to compare several of the proposed explanations in order to identify their advantages and disadvantages within the context of health lifestyle recommender systems. Our results should help recommender system designers, as well as users and researchers better understand the suitability of different recommendation explanations.

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