Analyzing Weighting Schemes in Collaborative Filtering: Cold Start, Post Cold Start and Power Users

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The document analyzes different weighting schemes in collaborative filtering to address the problem of popularity bias. It describes how standard collaborative filtering approaches can over-recommend popular items. The paper presents experiments testing two similarity weighting strategies (linear inverse and inverse user frequency) in different scenarios using two movie rating datasets. The results show that popularity weighting improves precision for users with a moderate number of ratings when the rating scale is compact, but not when the number of ratings is very low or very high.

Technology Entertainment & Humor

Analyzing Weighting Schemes in Collaborative Filtering:
Cold Start, Post Cold Start and Power Users

ACM SAC 2012
Alan Said, Brijnesh J. Jain, Sahin Albayrak
{alan, jain, sahin}@dai-lab.de
TU-Berlin

Outline

Movie Recommendation
Problem: Popularity Bias
Collaborative Filtering
Similarity Weighting Schemes
Experiments
Results
Conclusion

30.03.2012 2

Recommender Systems

What they should do:
Find items which should be of interest to users
Find items which should be useful to users

What they often do instead:
Find items which are known by users
Find items which users would have found anyway

30.03.2012 3

Popularity Bias

What is popularity bias?

Some things are more popular than others
Blockbuster movies1: Pulp Fiction, Inception, etc.
Best selling books2: Steve Jobs Bio, A Song of Ice
and Fire, etc.
Apps3: Angry Birds, Skype, Kindle

1
: IMDb most popular
2
: Amazon 2011 best sellers
3
: Most downloaded Android apps

30.03.2012 4

Popularity Bias

ed
rat
hly
= hig
ms
ite
lar
pu
Po

30.03.2012 6

Collaborative Filtering

Looks for users who share rating patterns
Use ratings from like-minded users to calculate a
prediction for the user
Boils down to:

The most similar users create a neighborhood.
Those items which are most popular in the neighborhood will be recommended.

30.03.2012 7

Collaborative Filtering: Similarities

Standard CF approaches do not consider the
popularity of items when creating neighbor-
hoods of similar users.
i.e. not considering the popularity bias.

Percentage of ratings given to
different popularity classes
of movies in the Movielens
10 Million ratings dataset

30.03.2012 8

Experiments

Approach: Test two similarity weighting strat-
egies in different scenarios on two different
movie rating datasets.

Weighting: Linear Inverse & Inverse User frequency

Datasets: Movielens10M & Moviepilot

Scenarios: Cold Start, Post Cold Start, Power Users

30.03.2012 11

Results

When is it good to use popularity weighting?
Movielens 10M

>20% improvement in Precision

Ratings: 1-5 stars

← 30 - 100 items each → 13
30.03.2012

Results

When is it not good to use popularity weighting?
Moviepilot

No significant improvement in Precision

Ratings: 0-10 stars

30.03.2012 14

Conclusion

Popular items create a problem for recom-
mender systems due to favorable bias.
Similarity weighting can lessen the effects of the
bias
when the rating scale is “compact”
when the users have “more than few” and “less than
many” ratings

30.03.2012 15

Ongoing Work

What if lower precision does not mean poorer
quality?
Lower precision can be an indicator of new, novel,
serendipitous recommendations – these will
produce lower precision values in offline evaluation
Currently evaluating the quality of recommender
algorithms based on user feedback, not only
precision/recall/etc. Values.
Users and Noise: The Magic Barrier of Recommender
Systems – UMAP'12
User satisfaction survey:
www.dai-lab.de/~alan/survey
30.03.2012 16

The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor quality data during training. Active learning aims to remedy this problem by focusing on obtaining better quality data that more aptly reflects a user’s preferences. In attempt to do that, an active learning strategy selects the best items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this seminar, I present a set of active learning strategies with different characteristics and the evaluation results with respect to several evaluation measures (i.e., MAE, NDCG, Precision, Coverage, Recommendation Quality, and, Quantity of the acquired ratings and contextual conditions). The traditional evaluation of active learning strategies has two major flaws: (1) Performance has been evaluated for each user independently (ignoring system-wide improvements) (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition). Addressing these flaws, I present that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system-centric). Hence, I present a novel offline evaluation methodology and use it to evaluate some novel and state of the art rating elicitation strategies. While the first set of experiments was done offline, the true value of active learning must be evaluated in an online setting. Hence, in the second part of the seminar, I present a novel active learning approach that exploits some additional information of the user (i.e. the user’s personality) to deal with the cold start problem in an up-and-running mobile context-aware RS called STS, that provides users with recommendations for places of interest (POIs). The results of live user studies, have shown that the proposed AL approach significantly increases the quantity of the ratings and contextual conditions acquired from the user as well as the recommendation accuracy.

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Analyzing Weighting Schemes in Collaborative Filtering: Cold Start, Post Cold Start and Power Users

1. Analyzing Weighting Schemes in Collaborative Filtering: Cold Start, Post Cold Start and Power Users ACM SAC 2012 Alan Said, Brijnesh J. Jain, Sahin Albayrak {alan, jain, sahin}@dai-lab.de TU-Berlin

2. Outline Movie Recommendation Problem: Popularity Bias Collaborative Filtering Similarity Weighting Schemes Experiments Results Conclusion 30.03.2012 2

3. Recommender Systems What they should do: Find items which should be of interest to users Find items which should be useful to users What they often do instead: Find items which are known by users Find items which users would have found anyway 30.03.2012 3

4. Popularity Bias What is popularity bias? Some things are more popular than others Blockbuster movies1: Pulp Fiction, Inception, etc. Best selling books2: Steve Jobs Bio, A Song of Ice and Fire, etc. Apps3: Angry Birds, Skype, Kindle 1 : IMDb most popular 2 : Amazon 2011 best sellers 3 : Most downloaded Android apps 30.03.2012 4

5. Popularity Bias 30.03.2012 5

6. Popularity Bias ed rat hly = hig ms ite lar pu Po 30.03.2012 6

7. Collaborative Filtering Looks for users who share rating patterns Use ratings from like-minded users to calculate a prediction for the user Boils down to: The most similar users create a neighborhood. Those items which are most popular in the neighborhood will be recommended. 30.03.2012 7

8. Collaborative Filtering: Similarities Standard CF approaches do not consider the popularity of items when creating neighborhoods of similar users. i.e. not considering the popularity bias. Percentage of ratings given to different popularity classes of movies in the Movielens 10 Million ratings dataset 30.03.2012 8

9. Collaborative Filtering: Similarities Standard CF approaches do not consider the popularity of items when creating neighborhoods of similar users. i.e. not considering the popularity bias. Disitribution of ratings given to the three most popular movies in the Movielens 10 Million dataset 30.03.2012 9

10. Weighting Schemes 30.03.2012 10

11. Experiments Approach: Test two similarity weighting strategies in different scenarios on two different movie rating datasets. Weighting: Linear Inverse & Inverse User frequency Datasets: Movielens10M & Moviepilot Scenarios: Cold Start, Post Cold Start, Power Users 30.03.2012 11

12. Results 30.03.2012 12

13. Results When is it good to use popularity weighting? Movielens 10M >20% improvement in Precision Ratings: 1-5 stars ← 30 - 100 items each → 13 30.03.2012

14. Results When is it not good to use popularity weighting? Moviepilot No significant improvement in Precision Ratings: 0-10 stars 30.03.2012 14

15. Conclusion Popular items create a problem for recommender systems due to favorable bias. Similarity weighting can lessen the effects of the bias when the rating scale is “compact” when the users have “more than few” and “less than many” ratings 30.03.2012 15

16. Ongoing Work What if lower precision does not mean poorer quality? Lower precision can be an indicator of new, novel, serendipitous recommendations – these will produce lower precision values in offline evaluation Currently evaluating the quality of recommender algorithms based on user feedback, not only precision/recall/etc. Values. Users and Noise: The Magic Barrier of Recommender Systems – UMAP'12 User satisfaction survey: www.dai-lab.de/~alan/survey 30.03.2012 16

17. Questions? Thank You! 30.03.2012 17

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