个性化推荐>> recommendations-toy-example>> 返回
项目作者： adrianmarino

项目描述 ：
Tourism recommendation system
高级语言： Jupyter Notebook
项目地址: git://github.com/adrianmarino/recommendations-toy-example.git


Tourism recommendation system

A toy example.

Requirements

Setup project

Step 1: Create conda environment to run project notebook.

  1. conda env create -f environment.yml

Step 2: Enable installed environment.

  1. conda activate tourism-recommendation-system

Step 3: Install a upyter extension required to support a progress bar in a notebook.

  1. jupyter labextension install @jupyter-widgets/jupyterlab-manager

Getting started Notebook

Step 1: Enable installed environment.

  1. conda activate tourism-recommendation-system

Step 2: run jupyter lab IDE:

  1. jupyter lab

Step 3: Open toy-example jupyter notebook.

Note: Can use query browser from http://localhost:7474.

Getting started API

Step 1: Enable installed environment.

  1. conda activate tourism-recommendation-system

Step 2: Start api server.

  1. ./start-api

Step 3: Query trends and recommendations.

More purchased hotels in last 60 days

  1. curl -X  GET "http://localhost:8080/api/recommendations/hotels/more-purchased?time-window=60" | json_pp
  1. {
  2.     "hotels": [
  3.         {
  4.             "destination": "SLA",
  5.             "name": "Posada Santana",
  6.             "score": 4
  7.         },
  8.         {
  9.             "destination": "RIO",
  10.             "name": "Hakuna Matata Hotel Bar",
  11.             "score": 3
  12.         },
  13.         {
  14.             "destination": "RIO",
  15.             "name": "Rio See Resort",
  16.             "score": 3
  17.         },
  18.         {
  19.             "destination": "BCN",
  20.             "name": "Barcelona Hotel",
  21.             "score": 2
  22.         },
  23.         {
  24.             "destination": "MIA",
  25.             "name": "Madagascar Palace",
  26.             "score": 1
  27.         }
  28.     ]
  29. }

More searched hotels in last 60 days

  1. curl -X  GET "http://localhost:8080/api/recommendations/hotels/more-searched?time-window=60" | json_pp
  1. {
  2.    "hotels" : [
  3.       {
  4.          "destination" : "MIA",
  5.          "score" : 23
  6.       },
  7.       {
  8.          "destination" : "BCN",
  9.          "score" : 20
  10.       },
  11.       {
  12.          "destination" : "SLA",
  13.          "score" : 15
  14.       },
  15.       {
  16.          "destination" : "RIO",
  17.          "score" : 10
  18.       },
  19.       {
  20.          "destination" : "COR",
  21.          "score" : 5
  22.       }
  23.    ]
  24. }

More purchased flights in last 60 days

  1. curl -X  GET "http://localhost:8080/api/recommendations/flights/more-purchased?time-window=60" | json_pp
  1. {
  2.    "flights" : [
  3.       {
  4.          "airline" : "LA",
  5.          "destination" : "SLA",
  6.          "score" : 3
  7.       },
  8.       {
  9.          "airline" : "AA",
  10.          "destination" : "RIO",
  11.          "score" : 2
  12.       },
  13.       {
  14.          "airline" : "LA",
  15.          "destination" : "RIO",
  16.          "score" : 2
  17.       },
  18.       {
  19.          "airline" : "EK",
  20.          "destination" : "BCN",
  21.          "score" : 1
  22.       },
  23.       {
  24.          "airline" : "AA",
  25.          "destination" : "MIA",
  26.          "score" : 1
  27.       }
  28.    ]
  29. }

More purchased flights in last 60 days

  1. curl -X  GET "http://localhost:8080/api/recommendations/flights/more-searched?time-window=60" | json_pp
  1. {
  2.     "flights": [
  3.         {
  4.             "destination": "SLA",
  5.             "score": 42
  6.         },
  7.         {
  8.             "destination": "BCN",
  9.             "score": 25
  10.         },
  11.         {
  12.             "destination": "RIO",
  13.             "score": 25
  14.         },
  15.         {
  16.             "destination": "MIA",
  17.             "score": 16
  18.         },
  19.         {
  20.             "destination": "COR",
  21.             "score": 5
  22.         }
  23.     ]
  24. }

Recommended hotels for users that bought flights for a given destination in last 60 days

  1. curl -X  GET "http://localhost:8080/api/recommendations/cross-selling/hotels?email=adrian.marino@almundo.com&time-window=60" | json_pp
  1. {
  2.     "hotels": [
  3.         {
  4.             "city": "SLA",
  5.             "id": "8",
  6.             "name": "Posada Santana",
  7.             "score": 4
  8.         },
  9.         {
  10.             "city": "RIO",
  11.             "id": "12",
  12.             "name": "Hakuna Matata Hotel Bar",
  13.             "score": 3
  14.         },
  15.         {
  16.             "city": "RIO",
  17.             "id": "10",
  18.             "name": "Rio See Resort",
  19.             "score": 2
  20.         },
  21.         {
  22.             "city": "MIA",
  23.             "id": "2",
  24.             "name": "See Palace Resort",
  25.             "score": 1
  26.         },
  27.         {
  28.             "city": "RIO",
  29.             "id": "11",
  30.             "name": "Pipa Hotel",
  31.             "score": 1
  32.         }
  33.     ]
  34. }

Recommended airlines for users that bought hotels in a given city in last 60 days

  1. curl -X  GET "http://localhost:8080/api/recommendations/cross-selling/airlines?email=adrian.marino@almundo.com&time-window=60" | json_pp
  1. {
  2.     "airlines": [
  3.         {
  4.             "destination": "SLA",
  5.             "name": "LATAM",
  6.             "score": 3
  7.         },
  8.         {
  9.             "destination": "RIO",
  10.             "name": "American Airlines",
  11.             "score": 2
  12.         },
  13.         {
  14.             "destination": "RIO",
  15.             "name": "LATAM",
  16.             "score": 2
  17.         },
  18.         {
  19.             "destination": "MIA",
  20.             "name": "American Airlines",
  21.             "score": 1
  22.         }
  23.     ]
  24. }