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asmuth
recommendify
Ruby2012–2013
asmuth / recommendify

recommendify

Generate recommendations using collaborative filtering

2Distinct people
2Mentions
0Explicit recommenders
1.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Generate recommendations using collaborative filtering

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month2 dated mentions

Observed mentions: 2012-02-19 05:23:00 UTC to 2013-01-21 03:08:11 UTC.

012012-02-01 00:00:00 UTC to 2012-03-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsFeb 20122012-03-01 00:00:00 UTC to 2012-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2012-04-01 00:00:00 UTC to 2012-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2012-05-01 00:00:00 UTC to 2012-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2012-06-01 00:00:00 UTC to 2012-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2012-07-01 00:00:00 UTC to 2012-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJul 20122012-08-01 00:00:00 UTC to 2012-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2012-09-01 00:00:00 UTC to 2012-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2012-10-01 00:00:00 UTC to 2012-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2012-11-01 00:00:00 UTC to 2012-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2012-12-01 00:00:00 UTC to 2013-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-01-01 00:00:00 UTC to 2013-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJan 2013

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Feb 20122012-02-01 00:00:00 UTC2012-03-01 00:00:00 UTC110
Mar 20122012-03-01 00:00:00 UTC2012-04-01 00:00:00 UTC000
Apr 20122012-04-01 00:00:00 UTC2012-05-01 00:00:00 UTC000
May 20122012-05-01 00:00:00 UTC2012-06-01 00:00:00 UTC000
Jun 20122012-06-01 00:00:00 UTC2012-07-01 00:00:00 UTC000
Jul 20122012-07-01 00:00:00 UTC2012-08-01 00:00:00 UTC000
Aug 20122012-08-01 00:00:00 UTC2012-09-01 00:00:00 UTC000
Sep 20122012-09-01 00:00:00 UTC2012-10-01 00:00:00 UTC000
Oct 20122012-10-01 00:00:00 UTC2012-11-01 00:00:00 UTC000
Nov 20122012-11-01 00:00:00 UTC2012-12-01 00:00:00 UTC000
Dec 20122012-12-01 00:00:00 UTC2013-01-01 00:00:00 UTC000
Jan 20132013-01-01 00:00:00 UTC2013-02-01 00:00:00 UTC101

What Hacker News says

2 sources
Well, my classifier works based on categories like ruby, programming, php, magento etc. To train the classifier I grabbed feeds from different reddits and used that as a based data set. What you are trying to achieve sounds more like a recommendation engine rather than a classifier maybe recommendify might come handy https://github.com/paulasmuth/recommendify You still can use the bayesian classifier, for training it I would recommend the supervised training route, basically start with a small dataset(100 records) and manually classify each of the training examples. Also you should leave some sort of way to provide feedback to your classifier to improve the results and make corrections
In Ask HN: Has anyone tried to write a Bayesian classifier for stories?

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