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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[CVPR 2022] Deep Equilibrium Optical Flow Estimation

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Good news! We release a clean version of PVNet: clean-pvnet, including how to train the PVNet on the custom dataset. Use PVNet with a detector. The tr

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This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm. It contains the code to reproduce the results presented in the original paper: https://arxiv.org/abs/2112.0

Saman Khamesian 6 Dec 13, 2022
Grammar Induction using a Template Tree Approach

Gitta Gitta ("Grammar Induction using a Template Tree Approach") is a method for inducing context-free grammars. It performs particularly well on data

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cim-misspelling Pytorch implementation of Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence, CHIL 2022. This model (

Juyong Kim 11 Dec 19, 2022
A lightweight library to compare different PyTorch implementations of the same network architecture.

TorchBug is a lightweight library designed to compare two PyTorch implementations of the same network architecture. It allows you to count, and compar

Arjun Krishnakumar 5 Jan 02, 2023
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MLJAR Automated Machine Learning Documentation: https://supervised.mljar.com/ Source Code: https://github.com/mljar/mljar-supervised Table of Contents

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Occlusion robust 3D face reconstruction model in CFR-GAN (WACV 2022)

Occlusion Robust 3D face Reconstruction Yeong-Joon Ju, Gun-Hee Lee, Jung-Ho Hong, and Seong-Whan Lee Code for Occlusion Robust 3D Face Reconstruction

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tinykernel - A minimal Python kernel so you can run Python in your Python

tinykernel - A minimal Python kernel so you can run Python in your Python

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Repository aimed at compiling code, papers, demos etc.. related to my PhD on 3D vision and machine learning for fruit detection and shape estimation at the university of Lincoln

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lelouedec 2 Oct 06, 2022
The Balloon Learning Environment - flying stratospheric balloons with deep reinforcement learning.

Balloon Learning Environment Docs The Balloon Learning Environment (BLE) is a simulator for stratospheric balloons. It is designed as a benchmark envi

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YouRefIt: Embodied Reference Understanding with Language and Gesture YouRefIt: Embodied Reference Understanding with Language and Gesture by Yixin Che

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In the AI for TSP competition we try to solve optimization problems using machine learning.

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