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系統識別號 U0026-2406202018315600
論文名稱(中文) 基於身心需求飲食模型的食物推薦多輪對話機器人
論文名稱(英文) Food recommendation multi-round chatbot based on dietary-physical and mental need-model
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 蔡政諺
研究生(英文) Zen-Yen Tsai
學號 P76074185
學位類別 碩士
語文別 英文
論文頁數 55頁
口試委員 指導教授-盧文祥
口試委員-楊中平
口試委員-梁勝富
口試委員-張景新
口試委員-張嘉惠
中文關鍵字 使用者需求抽取  飲食推薦  多輪對話機器人 
英文關鍵字 user need extraction  diet recommendation  multi-turn chatbot  
學科別分類
中文摘要 近年來,隨著人工智慧的普及,聊天機器人應用程式的發展也越來越快。如今,許多業者在社群平台上都擁有自己的聊天機器人,但是這些聊天機器人中的大多數只能處理一些常規任務,以餐飲業為例,他們的聊天機器人只能詢問用餐內容,餐廳訊息或進行預訂。但是,借助自然語言處理技術,能夠真正理解人類語言和需求的聊天機器人是我們所期待的。

在這篇論文中,我們希望可以建立一個飲食推薦系統,該系統可以在決定吃什麼或點菜時了解人類的需求,然後通過該系統建立一個多輪對話式聊天機器人。我們定義三種類型的使用者需求:疾病和症狀,心情和特殊需求。我們的系統將建立多個模型並收集多種資源,以在與使用者對話期間抽取這些使用者需求。最後,我們將根據所抽取出的身心和飲食需求向使用者推薦合適的食物清單。
英文摘要 In recent years, as the artificial intelligent become very popular, the development of chatbot application has also become faster. Nowadays, many stores have there own chatbot on the social community, but most of those chatbot can only handle some regular task, taking the catering as example, their chatbots can only asking the content of meals, information of the restaurant or making reservation. But with the Natural language processing skills, chatbots that can truly understand human’s language and needs are what we look forward to.

In our work, we hope we can build a diet recommendation system that can understand human’s needs when deciding what to eat or ordering food and then build a multi-round conversational chatbot by the system. We define three type of user needs: Disease & Symptom, emotion, and Special need. Our system will build multiple model and collect multiple resource to extract these user needs during the conversation with user. In the end, we will recommend a suitable food list to users according to the extracted physical, mental, and dietary needs.
論文目次 摘要 I
Abstract II
List of figures V
List of table VI
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Method 3
1.4 Goal 4
Chapter 2 Related work 5
2.1 Disease & symptom Detection 5
2.2 Emotion Analysis 6
2.3 Special need Detection 6
Chapter 3 Method 8
3.1 System Architecture 8
3.2 Disease & Symptom Detection 9
3.2.1 Medical terms Dictionary 9
3.2.2 Medical Entity Recognition (MER) model 10
3.3 Emotion Analysis 12
3.3.1 e-hownet 13
3.3.2 DLUT emotion ontology 14
3.3.3 Sentiment model 16
3.4 Special need detection 17
3.4.1 Hanlp Syntactic dependency parser 17
3.4.2 Patterns of dependency relation 19
3.4.3 Three-level-filtering for word pairs of special need 19
3.5 Food discovery & ranking 22
3.5.1 Food list 22
3.5.2 Food discovery of disease and symptom 24
3.5.3 Food discovery of emotion 25
3.5.4 Food discovery of special need 27
3.5.5 Ranking and recommendation 28
Chapter 4 Experiment 33
4.1 Disease & Symptom detection experiment 33
4.1.1 Dataset 33
4.1.2 Evaluation metric 34
4.1.3 Experiment result 35
4.2 Emotion Analysis experiment 37
4.2.1 Dataset 37
4.2.2 Evaluation metric 37
4.2.3 Experiment result 38
4.3 Special need detection experiment & food retrieval experiment 39
4.3.1 Dataset 39
4.3.2 Evaluation metric 40
4.3.3 Experiment result 40
4.4 Food discovery & ranking experiment 44
4.4.1 Experiment Setup 44
4.4.2 Evaluation metric 46
4.4.3 Experiment result 47
Chapter 5 Conclusion 49
Chapter 6 Application 50
Reference 54
參考文獻 [1] Jhih-Sheng Fan, Chatbot Application: Event driven Task-Oriented Store Recommendation.
[2] Wen-Chun Wu, A Health-preserving Suggestions Chatbot based on Disease Knowledge-graph.
[3] Jia-Chun Cai, Summary Generation for Chinese Patient Complaint based on Medical Entity Recognition and Medical Terminology Mapping.
[4] Vaswani et al., Attention Is All You Need.
[5] Gaël Letarte, et al., Importance of Self-Attention for Sentiment Analysis.
[6] Sheng-Tang Chuang, Product Recommendation Bot Based On Related Task Structure Using News Event and Yahoo Answer.
[7] Chen-An Wang, Shopping Chatbot based on Complex Task Structure and Consumption Need.
[8] Ting-Xuan Wang, Identifying Consumption Needs for Complex Search Tasks to Recommend Proper Advertisements.
[9] Zhiheng Huang, Wei Xu, Kai Yu. Bidirectional LSTM-CRF Models for Sequence Tagging
[10] Laura Canetti, Eytan Bachar. Elliot M. Berry, Food and emotion.
[11] Jorge Almeida. Personalized Food Recommendations Exploring Content-Based Methods.
[12] Burusothman Ahiladas, Paraneetharan Saravanaperumal, Sanjith Balachandran, Thamayanthy Sripalan and Surangika Ranathunga. Ruchi: Rating Individual Food Items in Restaurant Reviews.
[13] Tomas Mikolov, et al.. Distributed Representations of Words and Phrases and their Compositionality.
[14] John Lafferty, Andrew McCallum, Fernando C.N. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.
[15] Sepp Hochreiter, Jürgen Schmidhuber. Long Short-Term Memory.
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