the benefit of using tag-based profiles
DESCRIPTION
The Benefit of Using Tag-Based Profiles. Claudiu Firan, Wolfgang Nejdl, Raluca Paiu 5 th Latin American Web Congress, 2007. Music Recommendation. Personal Music. Community Data. Challenges. New Approach. Personal Music. Personal Tags. Community Data. Why Use Tags?. Tags are: - PowerPoint PPT PresentationTRANSCRIPT
The Benefit The Benefit of Using of Using
Tag-Based ProfilesTag-Based Profiles
Claudiu Firan, Wolfgang Nejdl, Raluca Paiu5th Latin American Web Congress, 2007
Music RecommendationMusic Recommendation
2
PersonalMusic
Community
Data
ChallengesChallenges
Collaborative Filtering
Content Based Techniques
Hybrid Methods
• Cold start problem• Items with no ratings• Users with no profile
• Poor artist variety in recommended pieces• Slow
• Unreliability in modeling user’s preferences• Content similarity does not necessarily reflect preferences• Slow
• Heavy user input
3
New ApproachNew Approach
4
PersonalMusic
Community
Data
PersonalTags
Why Use Tags?Why Use Tags?
Tags are:• Written chaotically• Not verified• Unstructured• Heterogeneous• Unreliable
But if many, the correct ones arise
“Wisdom of the masses”
5
Last.fm – “The Social Music Revolution”Last.fm – “The Social Music Revolution”
6
TrackTrack
ArtistArtist
Similar ArtistsSimilar Artists
AlbumsAlbums
Track Usage
Info
Track Usage
Info
Similar TracksSimilar Tracks
Tags(with weight)
Tags(with weight)
User Comments
User Comments
Tracks, Tags, and ProfilesTracks, Tags, and Profiles
7
User ProfilesUser Profiles
weight=preference(user,item)
8
Track-based Profiles (TR)Track-based Profiles (TR)
preference(user,track) = log(user_track_#listened)
9
TRTR
<tracki, weighti> …
<tracki, weighti> …
Track-Tag-based Profiles (TT)Track-Tag-based Profiles (TT)
preference(user,tag) = log( Σi(
log(user_tracki_#listened) ∙
log(user_tag_tracki_#tagged)))
[∙ ITF(tag)]
ITF = Inverse Tag Frequency• With: TTI• Without: TTN
10
TTNTTN
TTITTI
<tagi, weighti> …
<tagi, weighti> …
Tag-based Profiles (TG)Tag-based Profiles (TG)
preference(user,tag) = log(user_tag_#used)
11
TGTG
<tagi, weighti> …
<tagi, weighti> …
User Profiles from Personal MP3sUser Profiles from Personal MP3s
1. Read personal playlist from PC
2. Match MP3s against our database
3. Add overall average usage information values
12
Collaborative Filtering vs. SearchCollaborative Filtering vs. Search
13
Track- & Tag-based RecommendationsTrack- & Tag-based Recommendations
14
Collaborative Filtering
Collaborative Filtering
<tracki, weighti> …
<tracki, weighti> …
<tagi, weighti> …
<tagi, weighti> …
Tag-based SearchTag-based Search
15
<tagi, weighti> …
<tagi, weighti> …
AlgorithmsAlgorithms
16
Experiments & OutcomeExperiments & Outcome
17
Last.fm Crawled DataLast.fm Crawled Data
• 317,058 tracks
• 21,177 tags (most prominent ones are music genres)
• 289,654 users 12,193 listened at least 50 tracks and used at least 10 tags
18
Experimental SetupExperimental Setup
1. Create user profiles• 18 subjects• 658 tracks on average in user profile (not statistically
significant in influencing algorithm outcome)
2. Run algorithms• 7 algorithms• 10 recommended items per algorithm per user
3. Two scores• Quality of recommendation [0-2] NDCG• Novelty of recommendation [0-2] Average
19
ResultsResults
20
Nr
Algorithm
NDCG Signif. vs. CFTR
Average Novelty
Average Popularit
y
1 CFTR 0.54 - 1.39 15,177
2 CFTG 0.25 Highly 1.83 4,065
3 CFTTI 0.36 Highly 1.72 6,632
4 CFTTN 0.37 Highly 1.74 13,671
5 STG 0.60 No 1.07 7,587
6 STTI 0.73 Highly 0.82 10,380
7 STTN 0.77 Highly 0.78 16,309
CFTR: BaselineCFTR: Baseline
STG: • Lower popularity• Higher quality
STG: • Lower popularity• Higher quality
STTI & STTN: • Huge improvement• Statistically significant
STTI & STTN: • Huge improvement• Statistically significant
NDCG – Novelty: • High inverse correlation• Pearson c = -0.987
NDCG – Novelty: • High inverse correlation• Pearson c = -0.987
Gain over the Baseline (CF on Tracks)Gain over the Baseline (CF on Tracks)
21
ConclusionsConclusions
• CF on tag-based profiles worse than CF on track-based profiles
• Search with tags improved recommendation performance substantially• 44% increase in quality• Instant results – virtually no time delay• No cold start problem
• Tag-based profiles work also with less rich music repositories
• Results probably influenced by the consistent tag usage on Last.fm: mostly genres
22
