飲食保健推薦機制之設計與實作 - 以中國飲食療法為例

Abstract

隨著資訊發達，飲食保健療法及其他另類療法的觀念普及化，消費者的健康意識逐漸提昇，投資於身體機能保健、養生等意願更為積極，他們開始打破以往只吃藥打針的被動地位，開始關心自身健康狀況。而且，隨著網際網路的普及與便利性，消費者利用網路搜尋食物效用或評論的需求將會越來越多，未來對於飲食保健療法資訊與推薦的應用也將隨著網路的擴展受到消費者重視。然而，在目前現有的資訊系統中卻缺乏一套有效的推薦機制，以提供飲食保健療法(Dietary Therapy, DT)推薦。 有鑑於上述未來趨勢之需求，本研究提出一個以動態本體論(Dynamic Ontology, DO)為基礎去針對飲食保健療法(Dietary Therapy, DT)設計一個推薦機制 - 飲食保健療法推薦機制(Dietary Therapy Recommendation Mechanism, DTRM)，主要提供健康又安全的推薦服務。使用者可以透過本推薦機制，就能根據使用者自身期望的飲食需求或是使用者本身所患有的疾病，找到最適合自己需求的飲食保健療法種類，達到讓使用者均衡、健康飲食之目標。 本論文針對所提出的飲食保健療法推薦機制設計了兩個實驗，進行推薦結果準確率(Precision)的評估。實驗結果證明不論是實驗一針對羹湯類飲食保健療法進行推薦或是實驗二針對肉品類飲食保健療法進行推薦，使用飲食保健療法推薦機制(DTRM)去取代舊有方法後，推薦結果準確率(Precision)在16、64、256三種樣本數底下具有50％∼80％的準確性，相較於使用舊有的本體論方法，只有20％∼50％的準確率有著大幅的提昇。這證明了本論文所提出的一個以動態本體論為基礎所建制出來的飲食保健療法推薦機制比使用舊有的本體論所建制出來的推薦機制還要來的更優秀。

The rise of the quality of life index together with the improvement of medical technology lead to a longer life expectancy. Thus a better Health Diet Recommendation Service (HDRS), especially for elderly, is needed. However, to date, there are only a few Decision Support Systems (DSS) to provide HDRS for Dietary Therapy (DT) according to user’s diseases and retrieve the diet limitations. For this reasoning, we propose the Dynamic Ontology (DO) which includes Medical Ontology (MO) and Food Therapy Ontology (FTO) to build the HDRS. For ontology description and building, we refer ICD-CM (International Classification of Diseases, Clinical Modification) and dietitian’s recommendation to define and classify the diseases into MO and the foods into FTO, respectively. Moreover, we propose a curative food recommendation method, the Dietary Therapy Recommendation Mechanism (DTRM), which combines DO, Term Frequency–Inverse Document Frequency (TF-IDF), Latent semantic analysis (LSA), and Self-Organizing Map (SOM) for DT to provide the HDRS. The DTRM considers the user’s physiology state and diet preference to infer user’s diseases and retrieve the diet limitations according to DO. Afterward, The DTRM infers the optimum food collocation to provide relevant HDRS to user. In this paper, we design two test cases using Chinese food therapy to evaluate the DTRM. The Case 1 considers the “soup class” to provide the HDRS by DTRM, and the Case 2 considers the “meat class” for DT. The experimental results show that the recommendation precisions of DO-based DTRM and Static Ontology (SO)-based diet recommendation are 75.00% and 46.88% in Case 1. The recommendation precisions in Case 2 with DO and SO are 71.86% and 37.50%, respectively. Therefore, the DTRM based on DO is better than SO in both cases for DT.