It's a powerful version of linebot

Overview

CTPS-FINAL

Linbot-sever.py

主程式

Algorithm.py

推薦演算法,媒合餐廳端資料與顧客端資料

config.ini

儲存 channel-access-token、channel-secret 資料

Preface

生活在成大將近4年,我們每天的午餐時間看著形形色色的店家,看似玲瑯滿目卻都吃膩了,中午覓食已經從期待變成壓力,每天問著「待會吃什麼?」,然後花費大量時間和心力,還是不知道要午餐吃什麼。因此我們希望運用Computational Thinking and Problem Solving 的思維,幫助大家解決這個困擾已久的問題。

Problem Definition

My target problem - 解決成大師生不知道午餐吃什麼的困擾?

Problem Decomposition

  • :成大師生
  • :午餐煩惱
  • :週一到週五 11點 ~ 14點
  • :成大周遭 1.5km 以內距離
  • constrain : 交通限制(交通工具)、店家營業時間限制、用戶人數(餐廳是否能容納)、預計等待及用餐時間

Pattern Recognition

  1. 大家通常到正餐時間才會想要吃甚麼
  2. 大家移動的距離有限,如果下午1點還有課,就會在學校附近用餐
  3. 同類型食物太頻繁吃會吃膩
  4. 學生會考慮cp值(有價格區間考量)
  5. 如果店家以人潮眾多就傾向換一間店家
  6. 會因為天氣而影響選擇(例如很熱,就會找有冷氣的餐廳)
  7. 朋友或認識的同學會一起用餐

Abstraction

(把Problem Decomposition的細項問題化)

  • 店家資料
      1. 如何取得店家資料?
      1. 如何確保店家資料即時性?
  • 用戶資料
      1. 如何取得用戶資料?
      1. 如何做到使用者優化?
  • 演算法
      1. 如何根據實際狀況設計演算法
      1. 怎麼測試演算法結果是否符合用戶需求
  • 訊息回推
      1. 用什麼管道回送推薦清單
      1. 介面如何優化
      1. 怎麼得知用戶實際使用情況

Algorithm

  • 店家資料
    • 如何取得店家資料?
      • 利用 google maps 爬蟲
      • 實地探索(地點限制在成大周圍,所以有一定可行性)
    • 如何確保店家資料即時性?
      • 設計用戶回報機制
      • 定期網路爬蟲
  • 用戶資料
    • 如何取得用戶資料?
      • 利用 linbot 與使用者溝通,取得使用者需求
    • 如何做到使用者優化?
      • 利用 richmenus 串接 linbot,藉由圖文選單輸入
  • 演算法
    • 如何根據實際狀況設計演算法
      • 找外在生活條件(例如 : 天氣很熱,那冷氣的需求權重就提高一點)
    • 怎麼測試演算法結果是否符合用戶需求
      • 請朋友實際使用,並根據意見做出修改
  • 訊息回推
    • 用什麼管道回送推薦清單
      • Linebot
    • 介面如何優化
      • 建置模板按鈕,讓畫面看起來乾淨一點
    • 怎麼得知用戶實際使用情況
      • 設計用戶評分機制
      • 根據用戶評分或意見,進行修正

Solution Proposal

final report ppt & demo

References

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Di Chang 9 Nov 04, 2022
Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021]

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SwinIR: Image Restoration Using Swin Transformer

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Adversarially Learned Inference

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Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers

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ICON Lab 22 Dec 22, 2022
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Aditya Shah 1 Jan 03, 2022
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Hand Gesture Volume Control | Open CV | Computer Vision

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FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control

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