Tagommenders:Connecting Users to Items

Through Tags

Shilad SenMacalester College

Jesse Vig, John RiedlGroupLens Research

Tagommenders

1. Analyze user interactions to infer liking (preferences) for tag concepts.

2. Recommend items related to tag concepts liked by users.

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Tagommender Goals

• Recommend items using just tags. (Delicious)

• Improve item recommendations with ratings by by using tags. (LibraryThing / Amazon)• accuracy• flexibility• explainability (Vig, IUI 2009).

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Tagommender Flow ChartWALL-E

animation robots pixar

tag preference inference

tag-based recommendation

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MovieLens Tagging

• Tagging introduced in 2006• 15,000 distinct tags• 127,000 tag applications:

<user, tag, movie>• 4000 users applied >= 1

tag• 7700 movies with >= 1 tag

app

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Outline• Tag preference inference• Item recommendation• Auto-tagging and wrap-up

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Outline• Tag preference inference• Item recommendation• Auto-tagging and wrap-up

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Step 1: Tag Preference Inference

animationrobotspixar?

Infer a user’s interest in tags from:• tags user applied• tags user searched for• user’s clicks on movie hyperlinks• user’s movie ratings

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118,017 ratings

by 995 users

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Preferences for Tags Searched / Applied

average pref applied searched for0

1

2

3

4

5

Ave

rage

Tag

Pre

fere

nce

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Movie-rating algorithm

cars

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Movie-Rating Algorithm

cars

4 of 12 1 of 369 of 380.8 0.10.9

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Bayes-Rating Algorithm

Generative Model:• Expressive probabilistic processes.• Model movie ratings.• Separate model for every user, tag.

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Jill’s Ratings for animated Movies

0.0 1.0 2.0 3.0 4.0 5.00

0.2

0.4

0.6

0.8

1

animation

Star Rating for Movies With Tag

Freq

uenc

y

N(μ=3.8,σ=0.7)

Bayes-Rating Algorithm

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all possible normal dists for ratings for animated movies

WALL-E

not tt = animation

p(t | WALL-E) 1.0 - p(t | WALL-E)

N(μu,t,σu,t) N(μu,σu)

0 1 2 3 4 50

0.5

1

N(μ=2.0,σ=1.0)N(μ=4.0,σ=0.5)

Bayes-Rating Algorithm

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All movies m rated by Jill tagged with

animation

not tt = animation

Toy StoryWALL-E

Shrek

0 1 2 3 4 50

0.5

1

all possible normal dists for ratings for animated movies

Bayes-Rating Algorithm

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Outline• Tag preference inference• Item recommendation• Auto-tagging and wrap-up

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Tagommender Flow ChartWALL-E

animation robots pixar

tag preference inference

tag-based recommendation

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Step #2: Tag-Based Recommendation

• Standard machine learning problem• With / without ratings• Six standard recommender baselines• Evaluate predictive performance

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Outline• Tag preference inference• Item recommendation• Auto-tagging and wrap-up

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Inferred pref for girlie movie:

Rating for “Runaway Bride”

Alice

Bob

Mike

(other users) …. …

cosine similarity = 0.45

Using Tag Preferences for Tag Inference

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Top 10 Inferred Tags Not Already Applied

movie tag cosine simPearl Harbor (2001) disaster 0.47Runaway Bride (1999) girlie movie 0.45Beauty and the Beast (1991) talking animals 0.42Armageddon (1998) will smith 0.41Cinderella (1950) cartoon 0.40Inconvenient Truth (2006) documentary 0.40The Little Mermaid (1989) musical 0.40Gone in 60 Seconds (2000) exciting 0.39My Best Friend’s Wedding (1997) chick flick 0.39Billy Madison (1995) very funny 0.39

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Summary of Tagommenders

• Tag preference inference:• Systems can infer user preferences for tags.• Item ratings help tag pref inference.• Tag prefs can be used for auto-tagging.

• Tagommenders outperform traditional recommenders:• Without ratings: moderate edge (10%).• With ratings: slight edge (2%).

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Future Work

1. Alternative modalities for tags.2. Quality vs. preference.

Thank You!3. GroupLens.4. MovieLens users.5. NSF grants IS 03-24851 and IIS 05-34420.6. Macalester College.

Shilad Senssen@macalester.edu(photo by flickr user SantiMB)