Post on 24-Jan-2016
description
The Benefit of UsingTag-Based Profiles LA-Web 2007
2008/03/14
Claudiu S. Firan Wolfgang Nejdl Raluca PaiuL3S Research Center
Introduction
Collaborative tagging has emerged as an important way to organize, provide and share information about the resources on the Web.
Recent research has shown that such tag distributions stabilize over time, and can be used to improve search on the Web.
How can they be used to enable personalized recommendations? More specifically : Music recommendations.
Current System
Collaborative Filtering Cold start problem Poor variety
Content Similarity Similarity does not imply preference
Hybrid Methods Complex for general users
Tags not used for recommendation
Related Work
Most music recommender systems are based on collaborative filtering
Other approaches exist FOAF : friend-of-a-friend, RSS { users, ratings, contents } Bayesian network But,
Profiles not automatically inferred from music data Profiles are track based, not tag based
Similar approach Bookmarking recommendation on Del.icio.us
Functionalities & Usage Data ( Last.fm )
Track User Tag
Track Data ( Last.fm )
Track name Artist name Album name Tags & score Number of times has been played User comments
Track Data ( Last.fm )
Track Data ( Last.fm )
Track Data ( Last.fm )
Track Data ( Last.fm )
Track Data ( Last.fm )
Track Data ( Last.fm )
Track Data ( Last.fm )
Total 317,058 tracks
Tag Data ( Last.fm )
Number of times has been used Number of users have used Similar tags with scores
Tag Data ( Last.fm )
Tag Data ( Last.fm )
Total 21,177 tags
Tag Data ( Last.fm )
User Data ( Last.fm )
ID Gender Age Location Register date Number of tracks Friends, Neighbors, Groups Tags
User Data ( Last.fm )
User Data ( Last.fm )
Total 289,654 users Filter :
> 50 tracks > 10 tags
12,193 users left
User Profiles
Track-based A list of < track, score > pairs.
Tag-based A list of < tag, score > pairs.
User Profiles
Track-based Track-Tag-based Tag-based
Last.fm
user
Tracks that user played, and times
as score.
•Track list•Tags by all user•# times played (user) # times tagged (all)
Tags used by user# times tagged
(user)
Non-Last.f
muser
Tracks from user’s PC, and times played by all
Last.fm users.
•Track list•Tags by all user•# times played (all) # times tagged (all)
# tracks on user’s PC
( top 29 )
Music Recommendations
Collaborative Filtering based on Tracks baseline
Collaborative Filtering based on Tags CFTTI, CFTTN, CFTG
Search based on Tags STTI, STTN, STG
Music Recommendations
Track-based Track-Tag-based Tag-based
Collaborative Filtering
CFTR10 similar usercompute tracksrecommend
CFTTICFTTN
CFTG
Search
STTISTTN
STG
Music Recommendations
user
all user
similar
user
recommend track
Lucene similarit
y
tracktag
Music Recommendations
userR
all user
similar
user
Rrecommend track
Lucene similarit
y
tracktag
CFTR
Music Recommendations
userR
G
all userR
G
similar
userG
recommend track
Lucene similarit
y
R
tracktagCFTTI, CFTTN
CFTG
Music Recommendations
userR
G
all userR
G
recommend track
Lucene similarit
y
R
tracktag
STG
STTI, STTN
Evaluation
7 variant algorithms each returns 10 recommends
18 subject to rate Rating
Preference : 0, 1, 2 Novelty : 0, 1, 2
NDCG, Popularity, Novelty
Results - 1
Results - 2
Results - 3
Conclusions
Analyze tag usage of the most popular music community site, Last.fm
Compare user profiles based on tags with conventional based on tracks
Specify recommendation algorithms based on tag-based user profiles