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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

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
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
Tagommender Flow Chart

WALL-E

animation

robots

pixar

tag preference inference

tag-based recommendation


Movielens tagging
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
Outline

  • Tag preference inference

  • Item recommendation

  • Auto-tagging and wrap-up


Outline1
Outline

  • Tag preference inference

  • Item recommendation

  • Auto-tagging and wrap-up


Step 1 tag preference inference
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




Movie rating algorithm1
Movie-Rating Algorithm

cars

4 of 12

0.8

9 of 38

0.9

1 of 36

0.1


Bayes rating algorithm

Generative Model:

Expressive probabilistic processes.

Model movie ratings.

Separate model for every user, tag.

Bayes-Rating Algorithm


Jill s ratings for animated movies

Bayes-Rating Algorithm

Jill’s Ratings for animated Movies

N(μ=3.8,σ=0.7)


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)


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


Outline2
Outline

  • Tag preference inference

  • Item recommendation

  • Auto-tagging and wrap-up


Tagommender flow chart1
Tagommender Flow Chart

WALL-E

animation

robots

pixar

tag preference inference

tag-based recommendation


Step 2 tag based recommendation

Standard machine learning problem

With / without ratings

Sixstandard recommender baselines

Evaluate predictive performance

Step #2: Tag-Based Recommendation


Outline3
Outline

  • Tag preference inference

  • Item recommendation

  • Auto-tagging and wrap-up




Summary of tagommenders

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
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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