A User-based Collaborative Filtering Algorithm for Delicious Link Recommendation Engine Technology: pydelicious, Python, MySQL distance metric: Pearson Correlation Coefficient Step-1: Using the pydelicious.py API, I build a MySQL database of users and their posts and a rating, 1 or 0, based on whether they have posted the url or not Step-2: Just for the fun of it, I implemented a user-based Collaborative filtering method details (from sarwat et. al) The output of the algorithm is a list of recommended links for an input user of choice A sample output is: the link and the relative ranking indicating the user preference http://tele-task.de/archive/video/flash/14029/ 0.910118709912 http://gent.ilcore.com/2011/08/finding-memory-leaks.html 0.549350785728 http://ssssnakes.com/smoke/ 0.519390355699 https://github.com/lojjic/PIE 0.48942992567 https://github.com/laughinghan/choreographer 0.48942992567 https://github.com/coolaj86/futures 0.48942992567 https://github.com/cool-shark/redimon 0.48942992567 https://github.com/aikar/wormhole 0.48942992567 http://www.ipdusa.com/prodtype.asp?numRecordPosition=403 0.48942992567 http://sound.westhost.com/project100.htm 0.48942992567