Coen StevensLead Recommendation Engineer

How to build a recommender system?

Wakoopa use case

Mission:Discover software & games

MacWindows Linux

Software tracker

Your profile

Updates

Software pages

Recommendations

Building a recommender systemApproach and challenges

Data

(implicit) (explicit)

• Noisy

• Only positive feedback

• Easy to collect

• Accurate

• Positive and negative feedback

• Hard to collect

what do we have?

Usage Ratingsvs.

Datawhat do we use?

• Active users (Tracker activity in the past month): ~9.000

• Actively used software items (in the past month): ~10.000

• We calculate recommendations for each OS together with Web applications separately

Recommender system methods

• Item-based collaborative filtering

• User-based collaborative filtering (we only use for calculating user similarities to find people like you)

• Combining both methods

Collaborative recommendations: The user will be recommended items that people with similar tastes and preferences liked (used) in the past

Item-Based Collaborative FilteringUser software usage matrix

220 90 180 22

280 12 42 80

175 210 210 45

165 35 195 13 25

100 50 185 35 190

60 65 185

Users

Software items

User software usage matrix [0, 1]

1 1 0 1 0 1 0

1 1 1 0 1 0 0

1 1 0 1 0 1 0

1 0 1 1 1 1 0

0 1 1 1 0 1 1

0 1 0 1 0 0 1

Users

Software items

How do we predict the probability that I would like to use GMail?

1 1 0 1 0 1 0

1 1 1 0 1 0 0

1 1 1 0 1 0

1 0 1 1 1 1 0

0 1 1 1 0 1 1

0 1 0 1 0 0 1

Users

Software items

?

Calculate the similarities between Gmail and the other software items.

1 1 0 1 0 1 0

1 1 1 0 1 0 0

1 1 0 1 0 1 0

1 0 1 1 1 1 0

0 1 1 1 0 1 1

0 1 0 1 0 0 1

Users

Software items

Cosine Similarity(Firefox, Gmail)

Calculate the similarities between Gmail and the other software items.

1 1 0 1 0 1 0

1 1 1 0 1 0 0

1 1 0 1 0 1 0

1 0 1 1 1 1 0

0 1 1 1 0 1 1

0 1 0 1 0 0 1

Users

Software items

Cosine Similarity(Firefox, Gmail)

Calculate the similarities between Gmail and the other software items.

1 1 0 1 0 1 0

1 1 1 0 1 0 0

1 1 0 1 0 1 0

1 0 1 1 1 1 0

0 1 1 1 0 1 1

0 1 0 1 0 0 1

Users

Software items

Cosine Similarity(Firefox, Gmail)

Popularity correction, we put less trust

in popular software

Item-item correlation matrix

1 0.1 0.6 0.1 0.1 0.1 0.7

0.2 1 0.8 0.5 0.8 0.1 0.9

0.1 0.6 1 0.5 0.7 0.2 0.3

0.2 0.6 0.4 1 0.8 0.2 0.3

0.5 0.4 0.4 0.4 1 0.1 0.2

0.5 0.5 0.3 0.5 0.3 1 0.3

0.2 0.6 0.3 0.8 0.7 0.7 1

Item-item correlation matrix

1 0.1 0.6 0.1 0.1 0.1 0.7

0.2 1 0.8 0.5 0.8 0.1 0.9

0.1 0.6 1 0.5 0.7 0.2 0.3

0.2 0.6 0.4 1 0.8 0.2 0.3

0.5 0.4 0.4 0.4 1 0.1 0.2

0.5 0.5 0.3 0.5 0.3 1 0.3

0.2 0.6 0.3 0.8 0.7 0.7 1

0.6

0.8

0.4

0.4

0.3

0.3

Gmail similarities

K-nearest neighbor approach

Gmail similarities

• Performance vs quality

• We take only the ‘K’ most similar items (say 4)

• Space complexity: O(m + Kn)

• Computational complexity: O(m + n²)

0.6

0.8

0.4

0.4

0.3

0.3

Gmail similarities

1

1

1

1

Calculate the predicted value for Gmail

User usage

0.6

0.8

0.4

0.4

Gmail similarities

0.9

0.8

0.6

0.2

Calculate the predicted value for Gmail

User usage

0.6

0.8

0.4

0.4

Gmail similarities

Usage correction, more usage results

in a higher score [0,1]

(0.6 * 0.9) + (0.8 * 0.8) + (0.4 * 0.6)

0.6 + 0.8 + 0.4 + 0.4= 0.82

Gmail similarities User usage

Calculate the predicted value for Gmail

0.9

0.8

0.6

0.2

0.6

0.8

0.4

0.4

(0.6 * 0.9) + (0.8 * 0.8) + (0.4 * 0.6)

0.6 + 0.8 + 0.4 + 0.4= 0.82

Gmail similarities User usage

Calculate the predicted value for Gmail

0.9

0.8

0.6

0.2

0.6

0.8

0.4

0.4

• User feedback

• Contacts usage

• Commercial vs Free

Calculate all unknown values andshow the Top-N recommendations to each user

1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1 1

1 1 1 1 1

1 1 1

Users

Software items

?

?? ? ?

??

???

??

??? ?

?

ExplainabilityWhy did I get this recommendation?

• Overlap between the item’s (K) neighbors and your usage

User-Based Collaborative Filtering

1 1 0 1 0 1 0

1 1 1 0 1 0 0

1 1 0 1 0 1 0

1 1 1 1 1 1 0

0 1 1 1 0 1 1

0 1 0 1 0 0 1

Cosine Similarity(Coen, Menno)

Finding people like you

0.1 0.2 0 0.4 0 0.4 0

0.1 0.2 0.6 0 0.8 0 0

0.1 0.2 0 0.4 0 0.4 0

0.1 0.2 0.6 0.4 0.8 0.4 0

0 0.2 0.6 0.4 0 0.4 0.2

0 0.2 0 0.4 0 0 0.2

Cosine Similarity(Coen, Menno)

Applying inverse user frequency

log(n/ni): ni is the number of users that uses item i and n is the total number of users in the database

The fact that you both use Textmate tells you more than when you both use firefox

0.1 0.2 0 0.4 0 0.4 0

0.1 0.2 0.6 0 0.8 0 0

0.1 0.2 0 0.4 0 0.4 0

0.1 0.2 0.6 0.4 0.8 0.4 0

0 0.2 0.6 0.4 0 0.4 0.2

0 0.2 0 0.4 0 0 0.2

Cosine Similarity(Coen, Menno)

1 0.8 0.6 0.5 0.7 0.2

0.8 1 0.4 0.7 0.5 0.5

0.6 0.4 1 0.4 0.9 0.1

0.5 0.8 0.4 1 0.6 0.4

0.8 0.5 0.9 0.6 1 0.2

0.2 0.5 0.1 0.4 0.2 1

User-user correlation matrix

Performancemeasure for success

• Cross-validation: Train-Test split (80-20)

• Precision and Recall:- precision = size(hit set) / size(total given recs) - recall = size(hit set) / size(test set)

• Root mean squared error (RMSE)

Implementation

• Ruby Enterprise Edition (garbage collection)

• MySQL database

• Built our own c-libraries

• Amazon EC2: - Low cost- Flexibility- Ease of use

• Open source

Future challenges

• What is the best algorithm for Wakoopa? (or you)

• Reducing space-time complexity (scalability):- Parallelization (Clojure)- Distributed computing (Hadoop)

1 evening, 3 speakers, 100 developerswww.recked.org