The Foafing Music

Site: http://foafing-the-music.iua.upf.edu

I saved "Elvis Presley" as my favorite musician in my profile in www.blogger.com. The foafing music site recognize my favorite musician is "Elvis Presley" and recommend new music releases like below;

The recommend new music releases information retrieve from amazon.com and iTunes store. If I click the link, the page move the site that contains the original information. The new music releases information took over 30 second to retrieve the information and generate the recommend page.

ConTag: A Sematic Tag Recommendation System

Source Type: paper
Source URL: www.dfki.uni-kl.de/~sauermann/papers/adrian+2007a.pdf
This paper introduce the Contag approahch. It generates semantic tag recommendations for documents based on Semantic Web ontologies and Web 2.0 Services. They designed and implemented a process to normalize documents to RDF format, extract document topics using Web 2.0 services and finally match extracted topics to a Semantic Web ontology.
ConTag is based on a Semantic Tag Recommendation Process like below:


1. During the first step, Normalisation, the document’s content is tranformed to
RDF format to gain a fulltext description.
2. During the second step, Topic Extraction, topics are extracted by requesting
Web 2.0 services. This results in a topic map using SKOS vocabulary (Simple
Knowledge Organisation System)
3. The Alignment Generation is based on document classification methods.
For each topic in the topic map, several weighted alignment possibilities are
computed to retrieve similar things.
4. The forth step is called Alignment Execution. The alignment scheme is visualized as tag recommendations. The user decides whether to accept or reject
recommendations.

Related sites
-Extracting relevant keypahrases
http://tagthe.net
http://www.topicalizer.com
http://www.dfki.uni- kl.de/~horak/2006/contag
http://phaselibs.opendfki.de/wiki/AlignmentOntology

The Foafing music

Site: http://foafing-the-music.iua.upf.edu

The foafingmusic retrieve the my top 10 most listened artists from last.fm like below;

From my listening habits, the service detect three artists (Jessica Simpson, Radiohead, Rhapsody). Using there three artists information, the system recommend related artists,
a review related with Rhapsody which linked to rateyourmusic.com, MP3-blogs that include the Radiohead's music.
They find out the recommend data by filtering the three artists names from their RSS database.

A Privacy-preserving Collaborative Filtering Scheme with Two-way Communication

Source Type: Research paper
Source URL: http://portal.acm.org/ft_gateway.cfm?id=1134742&type=pdf&coll=GUIDE&dl=GUIDE&CFID=20955699&CFTOKEN=40773225

Summary: In traditional CF systems, a server first collects ratings from users and then executes CF algorithms to make recommendation. There is a serious threat to individual privacy since data collected from users cover personal information about places and things they do, watch, and purchases. To solve this issue, a randomization approach has been proposed to disguise user ratings while still producing accurate recommendations. However, recent research work[1] has point out that randomization techniques might not preserve privacy as much as had been believed. This paper introduce a two-way communication privacy-preserving scheme in which users perturb their ratings for each item based on the server's guidance instead of using an item-invariant perturbation. According to their experiment, their new scheme preserve more privacy information than the randomization approach at the same accuracy level.

Reference:
1. Deriving Private Information from Randomized Data