Tagommenders : Connecting Users to Items through Tags

Tagommenders: Connecting Users to Items through Tags

Written by Shilad Sen, Jesse Vig, and John Riedl (2009)

 Presented by Ken Hu and Hassan Hattab

Overview

• Introduction• Tagommender

o Philosophyo Dataset

• Tag Preference Inferenceo Approacho Methods

• Recommenderso Implicito Explicit

• Results• Conclusion

First, Recommenders. 

• What is Recommender system?• Two Main tasks

o Recommend.o Predict.

Recommender Systems 

• Types of recommender systems:o User-based: decides according to the user's

previous choiceso Item-based: decides according to related items

to a selected itemo SVD

• Problem: These methods don't consider the content of the item. 

• Solution: Content-based Recommenders 

Overview

• Introduction• Tagommender

o Philosophyo Dataset

• Tag Preference Inferenceo Approacho Methods

• Recommenderso Implicito Explicit

• Results• Conclusion

Tagging Systems   

• Uses tags to address (categorize) items to users• Tags are created by general users (More

meaningful )

Tagommenders: 

• Basically, they combine Recommenders (content-based) and tagging systems. 

• Two main parts for Tagommenders:o They infer users’ preferences for tags based on

their interactions with tags and movieso and they infer users’ preferences for movies

based on their preferences for tags.

Tagommender's data set

These are collected from the MovieLens website.

• Movie Rating • Movie clicks• Tag applications• Tag Searches •  Tag Preference Ratings

Tagommender's data set

Overview

• Introduction• Tagommender

o Philosophyo Dataset

• Tag Preference Inferenceo Approacho Methods

• Recommenderso Implicito Explicit

• Results• Conclusion

Tagommender's  Cycle

Inferring Tag Preference

• Inferring Preference using Tag Signals (Direct) 

Inferring Tag Preference

• Inferring Preference using Item Signals (indirect) 

Inferring Preference using Item Signals

• Sigmoid transformation is used to calculate the weight of movie m to tag t

Inferring Preference using Item SignalsMethods1.Movie-Clicks2.Movie-log-odds-clicks3.Movie-r-Clicks4.Movie-r-log-odds-clicks5.Movie-Rating6.Movie Bayes

1- Movie-Clicks:

set of movies clicked by user u

2- Movie-log-odds-clicks

3- Movie-r-Clicks4- Movie-r-log-odds-clicks

• The only difference is Movie-rating is counted rather than movie clicks 

5- Movie-Rating

• A user’s preference for a tag is the average rating for a movie under that tag. 

user u's rating for movie m

6- Movie-bayes

• A bayesian generative model for users rating for a certain tag.

• if the tag is relevant to a rating then the rating will be chosen from the user-tag-specific distribution

• Else, it will be chosen from the user background rating  distribution

Which one is better?

Overview

• Introduction• Tagommender

o Philosophyo Dataset

• Tag Preference Inferenceo Approacho Methods

• Recommenderso Implicito Explicit

• Results• Conclusion

Recommenders

• Implicito Tag data onlyo Recommend onlyo 2 algorithms

Implicit-tag Implicit-tag-pop

• Explicit Algorithmso Use users' movie ratingso Recommend and predicto 3 algorithms

Cosine-tag Linear-tag Regress-tag

Implicit : Implicit-tag

• Vector Space Modelo Inferred tag preferenceo Relevance weight

Implicit : Implicit-tag-pop

• Implicit-tag with movie popularity        

o Tag > clicks, clicker count > click counto Linear estimation of log function

Recommenders

• Implicito Tag data onlyo Recommend onlyo 2 algorithms

Implicit-tag Implicit-tag-pop

• Explicit Algorithmso Use users' movie ratingso Recommend and predicto 3 algorithms

Cosine-tag Linear-tag Regress-tag

Explicit : Cosine-tag

• Cosine similarity: rating vs tag preference

Explicit : Linear-tag

• Least-square fit linear regression

Explicit : Regress-tag

• Linear-tag with similarity between tags    • SVM was best to estimate h

o Robustness against overfitting

Overview

• Introduction• Tagommender

o Philosophyo Dataset

• Tag Preference Inferenceo Approacho Methods

• Recommenderso Implicito Explicit

• Results• Conclusion

Results : Background

• Comparisonso Top-5

Compare top five recommendations

o MAE Average error of

prediction

• Competitorso Overall-avg o User-avgo User-movie-avgo Explicit-itemo Implicit-itemo Funk-svdo Hybrid

Regress-tag + funk-svd

Results : Top-5

Results : MAE

Overview

• Introduction• Tagommender

o Philosophyo Dataset

• Tag Preference Inferenceo Approacho Methods

• Recommenderso Implicito Explicit

• Results• Conclusion

Conclusion

• Introduced recommender algorithms based on user suggested tags (Tagommenders)

• Best at recommendation tasks• Adds value at prediction tasks

o Hybrid predictors does very well• Other advantages

o Ease to explaino Algorithmic evaluation of tag quality

Questions?