{"action":"closed","issue":{"url":"https://api.github.com/repos/ibayer/fastFM/issues/67","repository_url":"https://api.github.com/repos/ibayer/fastFM","labels_url":"https://api.github.com/repos/ibayer/fastFM/issues/67/labels{/name}","comments_url":"https://api.github.com/repos/ibayer/fastFM/issues/67/comments","events_url":"https://api.github.com/repos/ibayer/fastFM/issues/67/events","html_url":"https://github.com/ibayer/fastFM/issues/67","id":172495878,"number":67,"title":"Feature extraction from fastFM","user":{"login":"vladimirlitvinyuk","id":12463878,"avatar_url":"https://avatars.githubusercontent.com/u/12463878?v=3","gravatar_id":"","url":"https://api.github.com/users/vladimirlitvinyuk","html_url":"https://github.com/vladimirlitvinyuk","followers_url":"https://api.github.com/users/vladimirlitvinyuk/followers","following_url":"https://api.github.com/users/vladimirlitvinyuk/following{/other_user}","gists_url":"https://api.github.com/users/vladimirlitvinyuk/gists{/gist_id}","starred_url":"https://api.github.com/users/vladimirlitvinyuk/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/vladimirlitvinyuk/subscriptions","organizations_url":"https://api.github.com/users/vladimirlitvinyuk/orgs","repos_url":"https://api.github.com/users/vladimirlitvinyuk/repos","events_url":"https://api.github.com/users/vladimirlitvinyuk/events{/privacy}","received_events_url":"https://api.github.com/users/vladimirlitvinyuk/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2016-08-22T16:26:02Z","updated_at":"2016-10-24T17:45:16Z","closed_at":"2016-10-24T17:45:16Z","body":"Hello\r\nThanks for a great tool. Could you, please, ask a couple of questions on its functionality?\r\nIs it somehow possible:\r\n- to extract weights/latent feature vectors from fastFM fitted model\r\n- to make feature space transformation to latent feature space, using fitted fastFM model.\r\n\r\nThe interesting part of the task - not only to predict actual ratings/probabilities, but also get some information on products and clients clusters.\r\n\r\nThanks a lot in advance,\r\nBest regards\r\nVladimir Litvinyuk\r\n"}}
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