README.md

@@ -22,7 +22,13 @@ The dataset, found in the `data/` folder, is scraped from user pages of [nomadli
 - Random (for reference and as a baseline)
     - randomly sample `n` cities from `cities` dataset
 - TripSimilarity using `trips` dataset
-    - TBD: Jirka
+
+    0. Groups cities based on user
+    1. Removes duplicities
+    2. Finds groups that satisfies user input (has same cities in group or biggest subsection if no group with full satisfaction)
+    3. From groups finds most cities with most occurrences (excluding user input)
+    4. Shuffle in groups positions (to avoid discrimination based on position)
+    5. Recommend first x (based on preferences)
 - Minkowski distance on `cities` with imputed data, using SimilarityCoefficient on `trips` dataset
 
     0. Impute the data - replace `-1` as missing values by most frequent value in that row

backend/app.py

@@ -2,6 +2,8 @@ from flask import Flask, render_template, url_for, request, redirect
 from flask_sqlalchemy import SQLAlchemy
 from datetime import datetime
 import csv
+
+from backend.views.RecommendationAlgorithmScore import scoreBlueprint
 from .models.UserCityModel import UserCityModel
 from .models.DestinationModel import DestinationModel
 from .models import db
@@ -36,6 +38,8 @@ def create_app(env_name):
     app.register_blueprint(userCityBlueprint)
     app.register_blueprint(recommendBlueprint)
     app.register_blueprint(recommendationsBlueprint)
+    app.register_blueprint(scoreBlueprint)
+
     with app.test_request_context():
         db.init_app(app)
         db.create_all()

backend/models/ScoreModel.py

+from . import db
+
+
+class ScoreModel(db.Model):
+    __tablename__ = "score"
+
+    algorithm_name = db.Column(db.String(200), primary_key=True, nullable=False, unique=True)
+    score = db.Column(db.Integer, nullable=False)
+
+    def __init__(self, data, algorithm_name, score):
+        self.algorithm_name = algorithm_name
+        self.score = score

backend/static/css/recommend.css

@@ -45,4 +45,27 @@ body {
     /*when navigating through the items using the arrow keys:*/
     background-color: DodgerBlue !important;
     color: #ffffff;
-  }
\ No newline at end of file
+  }
+
+div.info {
+     height:700px!important;
+     width: 45%!important;
+    position: relative;
+  margin-top: 30px;
+     float:right;
+     direction: ltr;
+    overflow:hidden;
+}
+.scroll {
+      unicode-bidi:bidi-override;
+      direction: rtl;
+      overflow-y: scroll;
+      overflow-x:hidden;
+}
+.info_inside{
+  position: absolute;
+    top: -65px;
+    left: -830px;
+    width: 1300px;
+    height: 1450px;
+}
\ No newline at end of file

backend/static/js/autocomplete.js

@@ -272,21 +272,47 @@ var ingredients = ['ho-chi-minh-city', 'seoul', 'alicante', 'amsterdam', 'london
      'ferrara', 'giza', 'bakersfield', 'lake-havasu-city', 'senggigi',
      'lansing', 'vladivostok', 'indore', 'lucknow'];
 
-  let elements;
 
-  var addMore = document.getElementById("add");
-  if (addMore.addEventListener) {
-      addMore.addEventListener("click", myFunction);
+  let addMoreAuto = document.getElementsByName("city_name");
+  addMoreAuto.forEach(el => {
+      assignListeners(el);
+  });
+
+  function assignListeners(element) {
+      autocomplete(element, ingredients);
+      element.addEventListener("input", autoAdder);
+      element.addEventListener("input", autoRemover);
   }
-  function myFunction(){
-      document.getElementById("first").insertAdjacentHTML('beforeend', "<input class='autocomplete' class='form-control' type='text' name='city_name'placeholder='Enter a city'/>");
-      elements = document.getElementsByClassName("autocomplete");
-      for (var i = 0; i < elements.length; i++){
-          autocomplete(elements[i], ingredients);
+
+  function autoAdder() {
+      let inputs = document.getElementsByName('city_name');
+      let allFull = true;
+
+      inputs.forEach(input => {
+          if (input.value == null || input.value === ""){
+              allFull = false
+          }
+      });
+
+      if (allFull){
+          document.getElementById("first").insertAdjacentHTML('beforeend', "<input class='autocomplete' class='form-control' type='text' name='city_name' placeholder='Enter a city'/>");
+          let els = document.getElementsByName("city_name");
+          let lastAdded = els[els.length - 1];
+          assignListeners(lastAdded);
       }
   }
 
-  elements = document.getElementsByClassName("autocomplete");
-  for (var i = 0; i < elements.length; i++){
-      autocomplete(elements[i], ingredients);
+  function autoRemover() {
+      let inputs = document.getElementsByName('city_name');
+
+      let first = true;
+      inputs.forEach(input => {
+          if (input.value == null || input.value === ""){
+              if (first){
+                  first = false;
+              }else{
+                  input.parentNode.removeChild(input);
+              }
+          }
+      });
   }
\ No newline at end of file

backend/static/js/general.js

 
 function createOption(){
     let newForm = document.createElement("OPTION");
+}
+
+
+function sendLike(elem, alg) {
+    let request = new XMLHttpRequest();
+
+    disableButtons(elem, "#b3ffe0")
+    // Open a new connection, using the GET request on the URL endpoint
+    request.open('GET', window.location.origin + '/like?alg=' + alg, true);
+
+    // Send request
+    request.send();
+}
+
+function sendDislike(elem, alg) {
+    let request = new XMLHttpRequest();
+    disableButtons(elem, "#bfbfbf")
+
+    // Open a new connection, using the GET request on the URL endpoint
+    request.open('GET', window.location.origin + '/dislike?alg=' + alg, true);
+
+    // Send request
+    request.send();
+
+}
+
+function disableButtons(elem, color){
+
+    elem.disabled = true;
+    elem.style.backgroundColor = color;
+    elem.parentNode.parentNode.style.backgroundColor = color;
+}
+
+function showInfo(city){
+    let iframe = document.getElementsByClassName("info_inside")[0];
+    iframe.parentNode.removeChild(iframe);
+
+    document.getElementsByClassName("info")[0].insertAdjacentHTML('beforeend', "<iframe class='info_inside' src='https://www.google.com/search?igu=1&ei=&q="+ city + "' frameborder='0' style='border:0' scrolling='no'></iframe>");
+
 }
\ No newline at end of file

backend/templates/index.html

 {% extends "bootstrap/base.html" %}
-{% block title %}This is an example page{% endblock %}
+{% block title %}Awesome Recommender{% endblock %}
 
 {% block navbar %}
 <div class="navbar navbar-fixed-top">

backend/templates/recommend.html

 {% extends "bootstrap/base.html" %}
-{% block title %}This is an example page{% endblock %}
+{% block title %}Awesome Recommender{% endblock %}
 
 {% block styles %}
 {{super()}}
@@ -9,46 +9,58 @@
 
 {% block scripts %}
 {{super()}}
-<script src="{{url_for('static', filename='js/autocomplete.js')}}"></script>
+<script src="{{ url_for('static', filename='js/autocomplete.js') }}"></script>
+<script src="{{ url_for('static', filename='js/general.js') }}"></script>
 
 {% endblock %}
 
 {% block content %}
 <h1 class="display-4">Travel destinations recommendations</h1>
 {% if not results %}
-<p>Enter a city / more cities that you've been to and liked</p>
+<p>Enter cities that you've been to and liked</p>
 <form action="/recommend/results" method="POST" autocomplete="off">
-    <divclass='col-xs-12 col-sm-12 col-md-10 col-lg-10'>
-        <div class='input-group', id='first'>
+    <div class='col-xs-12 col-sm-12 col-md-10 col-lg-10'>
+        <div class='input-group' , id='first'>
             <input class="autocomplete" class='form-control' type='text' name='city_name' placeholder="Enter a city"/>
         </div>
-    <button type="button" id="add" class="btn btn-light">Add a city</button>
-    <button type="submit" id="submit" value="submit" class="btn btn-light">Get recommendations</button>
+        <button type="submit" id="submit" value="submit" class="btn btn-light">Get recommendations</button>
 
     </div>
 </form>
 {% endif %}
 
 {% if results %}
-    <p>Input: {{cities}}</p>
-    {% for key, value in results.items() %}
-    <div class="col-xs-6">  
-        <h2 class="sub-header">{{key}}</h2>
-        <div class="table-responsive"></div>
-            <table class="table table-striped" >
-                <thead>
-                    <th>City</th>
-                    <th>Because you liked</th>
-                </thead>
-                {% for val in value %}
-                <tr>
-                    <td><a href="https://www.google.com/search?q={{ val[0] }}">{{val[0]}}</a></td>
-                    <td>{{val[2]}}</td>
-                </tr>
-                {% endfor %}
-            </table>
-        </div>
-    </div>
-    {% endfor %}
+<p>Input: {{cities}}</p>
+<div class="info">
+    <iframe class="info_inside"
+            src="https://www.google.com/maps/embed?pb=!1m18!1m12!1m3!1d3309.3312622412445!2d-83.37737678492407!3d33.958324180631536!2m3!1f0!2f0!3f0!3m2!1i1024!2i768!4f13.1!3m3!1m2!1s0x88f66cdecc8d0783%3A0x4f152e9d93fa5d6!2s170+College+Ave%2C+Athens%2C+GA+30601%2C+USA!5e0!3m2!1sen!2sie!4v1464026910465"
+             frameborder="0" style="border:0">
+    </iframe>
+</div>
+{% for key, value in results.items() %}
+<div class="col-xs-6">
+    <h2 class="sub-header">{{key}}</h2>
+    <div class="table-responsive"></div>
+    <table class="table table-striped">
+        <thead>
+        <th>City</th>
+        <th>Because you liked</th>
+        </thead>
+        {% for val in value %}
+        <tr>
+            <td><a href="https://www.google.com/search?q={{ val[0] }}" target="_blank" onmouseover="showInfo('{{val[0]}}')">{{val[0]}}</a></td>
+            <td>{{val[2]}}</td>
+            <td>
+                <button type="button" onclick="sendLike(this, '{{key}}')">+1</button>
+                <button type="button" onclick="sendDislike(this, '{{key}}')">-1</button>
+            </td>
+        </tr>
+        {% endfor %}
+    </table>
+</div>
+</div>
+{% endfor %}
+
+
 {% endif %}
 {% endblock %}
\ No newline at end of file

backend/views/RecommendationAlgorithmScore.py

+from flask import app, Blueprint, request
+from . import db
+
+from backend.models.ScoreModel import ScoreModel
+
+scoreBlueprint = Blueprint('score', __name__, url_prefix='')
+
+
+@scoreBlueprint.route('/like', methods=['GET'])
+def like():
+    alg = request.args.get("alg")
+    score = ScoreModel.query.get(alg)
+
+    score.score = score.score + 1
+    db.session.merge(score)
+    db.session.commit()
+    db.session.flush()
+
+    return "OK"
+
+
+@scoreBlueprint.route('/dislike', methods=['GET'])
+def dislike():
+    alg = request.args.get("alg")
+    score = ScoreModel.query.get(alg)
+
+    score.score = score.score - 1
+    db.session.merge(score)
+    db.session.commit()
+    db.session.flush()
+    return "OK"

backend/views/RecommendationResultsView.py

-from http import HTTPStatus
-from backend.models import db
-from flask import json, render_template, request, redirect, Blueprint, Response
-from ..models.UserCityModel import UserCityModel
-from ..models.DestinationModel import DestinationModel
-import sys
+from flask import render_template, request, Blueprint
 import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__))+ "/../../src/")
-from algorithms.CosineSimilarityWithSimilarityCoefficient import CosineSimilarityWithSimilarityCoefficient
-from algorithms.CosineSimilarity import CosineSimilarity
-from algorithms.CollaborativeFiltering import CollaborativeFiltering
-from algorithms.RandomSimilarity import RandomSimilarity
-from algorithms.TripSimilarity import TripSimilarity
-from algorithms.OtherSimilarity import OtherSimilarity
-from algorithms.OtherSimilarityWithSimilarityCoefficient import OtherSimilarityWithSimilarityCoefficient
+import sys
+
+from flask import render_template, request, Blueprint
+
+from src.algorithms.CosineSimilarityWithSimilarityCoefficient import CosineSimilarityWithSimilarityCoefficient
+from ..models.DestinationModel import DestinationModel
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../src/")
+from src.algorithms.CosineSimilarity import CosineSimilarity
+from src.algorithms.CollaborativeFiltering import CollaborativeFiltering
+from src.algorithms.RandomSimilarity import RandomSimilarity
+from src.algorithms.TripSimilarity import TripSimilarity
+from src.algorithms.OtherSimilarityWithSimilarityCoefficient import OtherSimilarityWithSimilarityCoefficient
 
 recommendationsBlueprint = Blueprint(
     'recommendations', __name__, url_prefix='')
 
 @recommendationsBlueprint.route('/recommend/results', methods=['POST'])
 def recommendationsResults():
-    
     cities = request.form.getlist("city_name")
     result = DestinationModel.query.all()
     available_cities = [str(r).split(" ")[1].split(">")[0] for r in result]
@@ -46,7 +45,9 @@ def recommendationsResults():
 
     if len(cities) != 0:
         recommendations = {
-            masking_dict["Manhattan iteratively imputed data with SimilarityCoefficient"]: other_sim_manhattan.similarity_on_iteratively_imputed_data(cities), 
+            masking_dict[
+                "Manhattan iteratively imputed data with SimilarityCoefficient"]: other_sim_manhattan.similarity_on_iteratively_imputed_data(
+                cities),
             masking_dict["SVD"]: collab.similarity_with_SVD(cities),
             masking_dict["Random"]: random.random_similarity(cities),
             masking_dict["Cosine Raw data with SimilarityCoefficient"]: cosine_sim.similarity_on_raw_data(cities),

backend/views/RecommendationView.py

@@ -5,7 +5,7 @@ from ..models.UserCityModel import UserCityModel
 
 recommendBlueprint = Blueprint('recommend', __name__, url_prefix='')
 
+
 @recommendBlueprint.route('/recommend', methods=['GET'])
 def recommendInput():
-
-    return render_template("recommend.html")
\ No newline at end of file
+    return render_template("recommend.html")

src/algorithms/CollaborativeFiltering.py

-from algorithms.recommender import *
-from algorithms.helpers import *
+from src.algorithms.recommender import *
+from src.algorithms.helpers import *
 from scipy.sparse.linalg import svds 
 #from recommender import *
 #from helpers import *

src/algorithms/CosineSimilarity.py

-from algorithms.recommender import *
-from algorithms.helpers import *
+from src.algorithms.recommender import *
+from src.algorithms.helpers import *
 
 class CosineSimilarity():
 

src/algorithms/CosineSimilarityWithSimilarityCoefficient.py

-from algorithms.recommender import *
-from algorithms.helpers import *
+from src.algorithms.recommender import *
+from src.algorithms.helpers import *
+
+from src.algorithms.helpers import load_data
+
 
 class CosineSimilarityWithSimilarityCoefficient():
 

src/algorithms/OtherSimilarity.py

-from algorithms.recommender import *
-from algorithms.helpers import *
+from src.algorithms.recommender import *
+from src.algorithms.helpers import *
 
 class OtherSimilarity():
 

src/algorithms/OtherSimilarityWithSimilarityCoefficient.py

-from algorithms.recommender import *
-from algorithms.helpers import *
+from src.algorithms.recommender import *
+from src.algorithms.helpers import *
 
 class OtherSimilarityWithSimilarityCoefficient():
 

src/algorithms/RandomSimilarity.py

-from algorithms.recommender import *
-from algorithms.helpers import *
+from src.algorithms.recommender import *
+from src.algorithms.helpers import *
 
 class RandomSimilarity():
 

src/algorithms/TripSimilarity.py

@@ -4,7 +4,7 @@ import random
 from typing import List, Dict
 from sympy.utilities.iterables import multiset_permutations
 import pandas as pd
-from algorithms.helpers import *
+from src.algorithms.helpers import *
 #from helpers import *
 """
 DEBUG if true, there will be small debug logs (on system.in)