Tagommenders connecting users to items through tags
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Tagommenders : Connecting Users to Items Through Tags. Shilad Sen Macalester College Jesse Vig , John Riedl GroupLens Research. Tagommenders Analyze user interactions to infer liking (preferences) for tag concepts. Recommend items related to tag concepts liked by users.

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Tagommenders : Connecting Users to Items Through Tags

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Tagommenders:Connecting Users to Items Through Tags

Shilad Sen

Macalester College

Jesse Vig, John Riedl

GroupLens Research


Tagommenders

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

Recommend items related to tag concepts liked by users.


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).


Tagommender Flow Chart

WALL-E

animation

robots

pixar

tag preference inference

tag-based recommendation


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


Outline

  • Tag preference inference

  • Item recommendation

  • Auto-tagging and wrap-up


Outline

  • Tag preference inference

  • Item recommendation

  • Auto-tagging and wrap-up


Step 1: Tag Preference Inference

?

animation

robots

pixar

  • 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


118,017 ratings

by 995 users


Preferences for Tags Searched / Applied


Movie-rating algorithm

cars


Movie-Rating Algorithm

cars

4 of 12

0.8

9 of 38

0.9

1 of 36

0.1


Generative Model:

Expressive probabilistic processes.

Model movie ratings.

Separate model for every user, tag.

Bayes-Rating Algorithm


Bayes-Rating Algorithm

Jill’s Ratings for animated Movies

N(μ=3.8,σ=0.7)


  • Bayes-Rating Algorithm

WALL-E

p(t| WALL-E)

1.0 - p(t| WALL-E)

t = animation

not t

all possible normal dists for ratings for animated movies

N(μu,σu)

N(μu,t,σu,t)

N(μ=2.0,σ=1.0)

N(μ=4.0,σ=0.5)


  • Bayes-Rating Algorithm

All movies m rated by Jill tagged with animation

t = animation

not t

all possible normal dists for ratings for animated movies

Toy Story

WALL-E

Shrek


Outline

  • Tag preference inference

  • Item recommendation

  • Auto-tagging and wrap-up


Tagommender Flow Chart

WALL-E

animation

robots

pixar

tag preference inference

tag-based recommendation


Standard machine learning problem

With / without ratings

Sixstandard recommender baselines

Evaluate predictive performance

Step #2: Tag-Based Recommendation


Outline

  • Tag preference inference

  • Item recommendation

  • Auto-tagging and wrap-up


Using Tag Preferences for Tag Inference


Top 10 Inferred Tags Not Already Applied


Tag preference inference:

Systems can infer user preferences for tags.

Item ratings help tag prefinference.

Tag prefs can be used for auto-tagging.

Tagommenders outperform traditional recommenders:

Without ratings: moderate edge (10%).

With ratings: slight edge (2%).

Summary of Tagommenders


Future Work

Alternative modalities for tags.

Quality vs. preference.

Thank You!

GroupLens.

MovieLens users.

NSF grants IS 03-24851 and IIS 05-34420.

Macalester College.


Shilad Sen

[email protected]

(photo by flickr user SantiMB)


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