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

The Foafingmusic2

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

To make my music list, I have visited last.fm several time and listed some musics. After that, I returned to the Foafing music site and updated the my music profile. The Foafing music site connected to the last.fm and retrieved the music list that I have listen in last.fm and generate the following information.

From the my listening habits, the system detected the two artists (Jessica Simpson, Rhapsody) and it provides related menu on left site.

The left menu provides information that related this two singer who was found in my listing list in last.fm.
I will explore left site menu next time.

An Effective Approach for Periodic Web Personalization

Resource type: paper
Resource url: http://portal.acm.org/citation.cfm?id=1248823.1249113&coll=&dl=

According to the paper, periodic web personalization aims to recommend the most relevant resources to a user during a specific time period by analyzing the periodic access patterns of the user from web usage logs. To support effective periodic web personalization, the author construct a user behavior model, called Personal Web Usage Lattice, from the web usage logs using the Fuzzy Formal Concept Analysis Technique. And then, the author deduces the resource that the user is most probably interested in during a given period. In the experimental, the author generates personalized resource for a set of predefined period conditions and measure the performance based on the applicability and satisfaction measures with respect to the different durations of period conditions and the different number of personalized resource (from 1 to 6). The experimental results shows that the proposed approach has achieved very effective web personalization evaluated by the applicability and satisfaction measures for predefined period conditions.

The Foafingmusic

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

The Foafing the music is a music recommender system based on user's profile

Based on your FOAF profile and your listening habits, foafing the music recommends the following things:

-similar artists to the ones you like
-new music releases from iTunes, Amazon, Yahoo, etc
-Album reviews from your artists and from recommended artists
-MP3-blogs to download music
-Postcast sessions to stream/download
-Automatic creation of playlist based on (only!) audio similarity
-Incoming concerts near to where you live!

When I create a new account, the system asked for my username or URL of three websites (Livejounal or Bloger, last.fm, and Webjay). From these three sites, they grab some music related information from my profiles.

Before I provide the URL of my blog profile page, I inputed "Dance, R&B, Latin" in favorite music and then saved the profile which is save in FOAF (the Friend of a Friend). From the FOAF profile in the blog site, the site get my favorite music information .

The site also request the user ID in Last.fm site. Last.fm builds a profile of my musical taste using a plugin for my media player. From this site the system gets noticed of music that I recently played.

At last, the site request the user ID in WebJay which is a tool that helps user listen to and publish web playlists. But currently, this service is closed by Yahoo.com

After I finish to create the account, the site took a time to retrieve my profile information from blogger.com site and last.fm site and showed my interests that retrieve from my profile.

The site provides three pages based on the my profile like below

- my artists page contains all the artists detected in my FOAF profile
- my music pages contains all the artists detected from my listening habits (from Audioscrobbler)
- my playlist page contains all the playlists that you have in Webjay (It is not working since the Webjay site is close)

In the artists page , the site showed that artist detected in my FOAF profile is "Dance". It seems the site recognize "Dance" as an artist name and showed one related artist "Shannon", so I added "Michael Jackson", "Elvis Presley" in my favorite music in blogger site and regenerated the my artists information in recommendation site. After that, the site showed that artists detected in my FOAF profile: Dance, Michel Jackson, Elvis Presley and provides following menu:
-related artists
-new releases
-reviews
-MP3-blogs
-Podcasts
-similar music

In the related artists menu, it show more than 30 related artists but I'm not sure what relationship are between them. I guess their music genre(Rock/pop) is same. If I click one of them, the site forward to MP3.com site and show the artist information.