Crowd-sourced Recommender Demo

Recommender Demo – click here!

This demo of a recommender is to illustrate an example of how a website (online music, e-commerce, news) generates recommendations to increase engagement and conversions.

This is not production ready, merely a POC of how it works.

* user selects favorite activities
* data is passed to server and processed in hadoop
* user can go to results page and select an activity to get recommendations

At this point, an auto-workflow has not been built, so there are a series of steps to create the new dataset. Here are the general steps:

1. user data feeds into database via website (which is used in generating recommendations)
2. data is moved and process in Hadoop
3. data is moved to MySQL, accessible using PHP
4. user selects an activity, and the crowd-sourced recommendations are displayed

Example: How Crowd-Sourcing Works (co-occurrence recommendations) Using Activities

All Users Activity History
| Activity | Art Fair | Fishing | Shovel Snow | Wedding |
| Jon          | Yes           | Yes         | Yes                      | No              |
| Jane        | No            | Yes         | No                        | Yes            |
| Jill            | Yes           | Yes         | No                        | Yes            |

A New User like to go to Weddings, and we need to recommend them other activities:
* Find Wedding in History Matrix who also enjoyed Wedding to it: U{Jane, Jill}
* Identify other activities same users (U) enjoyed, and rank by count

Recommendation
| Activity | Rank | Count of User (co-occurrence |
| Fishing  |  1         |  2                                                               |
| Art Fair |  2         | 1                                                                |