1. Methods and Metrics for Cold-Start Recommendations
Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar and David M. Pennock

2. Introduction Recommender System
suggest items of interest based on previous explicit/implicit ratings
Ratings
explicit rating
by user feedback
implicit rating
by user action (purchase, refer, click links ... )
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3. Collaborative Filtering by recommendations on community preferences
Content-based Filtering
matching user (id, query, demographic info.) with items
Cold-Start problem
when no one rated a specific item yet.
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4. evaluation of 2 algo. in cold-start setup propose CROC curve metric
In this paper,
evaluation of 2 algo. in cold-start setup
propose CROC curve metric
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5. Background and Related Work
early recommenders
pure collaborative
similarity-weighted average (memory-based algo.)
hard-clustering users
hard-clustering users and items
soft-clustering users and items
singular value decomposition
inferring item-item similarities
probabilistic modeling
list-ranking
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6. hybrid recommender systems Evaluations
Recently,
hybrid recommender systems
combining collaborative and content information
Evaluations
MAE, ROC, ranked list metrics, variants of precision/recall statistics
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7. 3. The Two-Way Aspect Model
designed for contingency table smoothing
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8. 3.1 Pure Collaborative Filtering Model
Aspect model
encodes a probability distribution over each person/movie pair.
have a hidden or latent cause z that motivates person p to watch movie m.
m is assumed independent of p given knowledge of z.
P(p,m) : smoothed estimates of the probability distribution of the contingency table
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9. the parameters are calculated using EM algorithm
# of latent vars : using performance training data
learning by maximizing P(p,m)
prediction by
Folding In
in person/actor model, we must create a new movie object out of the set of actors that appear in that movie
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11. 4. Naive Bayes Recommender
bag-of-words naive Bayes classifier applied to person/actor data
pure content-based method
Rating prediction :
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12. 5.Testing Methodology MovieLens data set
Each person rates at least twenty movies
rating : 1-5
actor & director info. from
test set of 19,192 out of 312,133
testing modes
implicit rating prediction
rating prediction
rating imputation
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13. 6. Evaluation Metrics
Receiver Operator Characteristic (ROC) curve [Herlocker]
showing hit/miss rates for different classification thresholds
sensitivity (hit rate)
percentage of all positive values found above some threshold
1-specificity (miss rate)
the fraction of all negative values found above some threshold
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14. Global ROC (GROC) curve
when allowed to recommend more to some users than others
1. Order pred(pi, mj) by magnitude.
2. Pick n, hit/miss rate.
Customer ROC (CROC) curve
when constrained to recommend the same number to each user.
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15. 7. Results
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