{"action":"opened","issue":{"url":"https://api.github.com/repos/ill-fated-ducks/Bangazon-Site/issues/23","repository_url":"https://api.github.com/repos/ill-fated-ducks/Bangazon-Site","labels_url":"https://api.github.com/repos/ill-fated-ducks/Bangazon-Site/issues/23/labels{/name}","comments_url":"https://api.github.com/repos/ill-fated-ducks/Bangazon-Site/issues/23/comments","events_url":"https://api.github.com/repos/ill-fated-ducks/Bangazon-Site/issues/23/events","html_url":"https://github.com/ill-fated-ducks/Bangazon-Site/issues/23","id":252993785,"number":23,"title":"User can see recommended products","user":{"login":"stevebrownlee","id":218592,"avatar_url":"https://avatars3.githubusercontent.com/u/218592?v=4","gravatar_id":"","url":"https://api.github.com/users/stevebrownlee","html_url":"https://github.com/stevebrownlee","followers_url":"https://api.github.com/users/stevebrownlee/followers","following_url":"https://api.github.com/users/stevebrownlee/following{/other_user}","gists_url":"https://api.github.com/users/stevebrownlee/gists{/gist_id}","starred_url":"https://api.github.com/users/stevebrownlee/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/stevebrownlee/subscriptions","organizations_url":"https://api.github.com/users/stevebrownlee/orgs","repos_url":"https://api.github.com/users/stevebrownlee/repos","events_url":"https://api.github.com/users/stevebrownlee/events{/privacy}","received_events_url":"https://api.github.com/users/stevebrownlee/received_events","type":"User","site_admin":false},"labels":[{"id":676648632,"url":"https://api.github.com/repos/ill-fated-ducks/Bangazon-Site/labels/enhancement","name":"enhancement","color":"84b6eb","default":true}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2017-08-25T19:13:08Z","updated_at":"2017-08-25T19:13:08Z","closed_at":null,"body":"# Bangazon Recommendation Engine\r\n\r\n## What You Will Be Learning\r\n\r\n## Sets\r\n\r\nYou will learn how to use unions and intersection on sets ([HashSet](https://msdn.microsoft.com/en-us/library/bb359438.aspx) in C#) to determine if a particular customer should have certain products in their Recommendation List.\r\n\r\n##### Like Similarity Coefficient in Two Sets\r\n\r\n![Simple similarity](https://raw.githubusercontent.com/nashville-software-school/bangazon-inc/master/projects/images/base-jaccard.png)\r\n\r\n##### Dislike Similarity Coefficient in Two Sets\r\n\r\n![Simple difference](https://raw.githubusercontent.com/nashville-software-school/bangazon-inc/master/projects/images/base-jaccard-dislikes.png)\r\n\r\n##### Dislike+Like Similarity Coefficient in Two Sets\r\n\r\n![Difference and similarity coefficient](https://raw.githubusercontent.com/nashville-software-school/bangazon-inc/master/projects/images/like%2Bdislike.png)\r\n\r\n##### Dislike+Like Similarity+Difference Coefficient in Two Sets\r\n\r\n![Difference and similarity coefficient](https://raw.githubusercontent.com/nashville-software-school/bangazon-inc/master/projects/images/like%2Bdislike%2Bdifferences.png)\r\n\r\n##### Possibility Coefficient\r\n\r\n![Possibility coefficient](https://raw.githubusercontent.com/nashville-software-school/bangazon-inc/master/projects/images/possibility.png)\r\n\r\nThis looks complex at first blush, but you'll discover that the steps with become clearer as you work it out.\r\n\r\n## Requirements\r\n\r\n1. When a customer first authenticates, the first view should display two lists.\r\n    1. If there are any items currently in an open order, display those products.\r\n    1. List any products that have a Possibility Coefficient great than 0.25.\r\n1. On the product detail view, add an affordance for a customer to like, or dislike, a product.\r\n1. If a customer has ordered a product, then it should be registered as a like, regardless if the customer performed the gesture on the product detail page.\r\n\r\n## Resources\r\n\r\n* The [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index)\r\n* [Article](https://www.toptal.com/algorithms/predicting-likes-inside-a-simple-recommendation-engine) describing the general process, but with Node.js.\r\n\n"}}
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