the benefit of using tag-based profiles

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

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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

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New ApproachNew Approach

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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”

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Last.fm – “The Social Music Revolution”Last.fm – “The Social Music Revolution”

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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

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User ProfilesUser Profiles

weight=preference(user,item)

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Track-based Profiles (TR)Track-based Profiles (TR)

preference(user,track) = log(user_track_#listened)

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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

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TTNTTN

TTITTI

<tagi, weighti> …

<tagi, weighti> …

Tag-based Profiles (TG)Tag-based Profiles (TG)

preference(user,tag) = log(user_tag_#used)

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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

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Collaborative Filtering vs. SearchCollaborative Filtering vs. Search

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Track- & Tag-based RecommendationsTrack- & Tag-based Recommendations

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Collaborative Filtering

Collaborative Filtering

<tracki, weighti> …

<tracki, weighti> …

<tagi, weighti> …

<tagi, weighti> …

Tag-based SearchTag-based Search

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<tagi, weighti> …

<tagi, weighti> …

AlgorithmsAlgorithms

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Experiments & OutcomeExperiments & Outcome

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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

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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

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ResultsResults

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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)

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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

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