This slide shows what we are interested in and what we will do near future.

1. Comparison of Pin Recommendation
Algorithms for Pinterest
Kentaro Adachi（Osaka Univ.）
Yoshinori Hijikata（Osaka Univ.）
Joseph A. Konstan（University of Minnesota）
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2. Outline
1. Introduction
• Background
• Related Works
2. Dataset
3. Recommendation Algorithms
4. Evaluation Metrics
5. Results
6. Summary and Future Work
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3. 1. Introduction
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4. Popular Web Services
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• Collecting images and movies
• Creating and editing
collections
Social Networking Service (SNS)
(Twitter, Facebook etc.)
• Uploading text and images
• Share, Like, comment
• Following others
Curation Service
(Instapaper, Scoop.it etc.)
Theme 1 Theme 2
Collect items
that match
a specific theme

5. Social Curation Service (including Pinterest)
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• Uploading text and images
• Share, Like, comments
• Following others
• Collecting images and movies
• Creating and editing collections
SNS Curation Service
＋
Function of SNS and Curation Service are both exist
share
Like
comment
follow
Collect images
that match
a specific theme
Theme A Theme B
Theme D
Theme E
Theme C

6. Pinterest (User’s Page)
6
Board
(*):2013/7/10
(**):2015/3/31
70 million users(*)
1 billion collections(**)
50 billion images(**)
Pin

7. Necessity of Recommender Systems
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Users need Recommender Systems
Difficult to find images that match users’ intention
Theme 1
Browsing
oneself
50 billion
images*
* March 31th 2015
Recommender
system

8. Users’ Usage Objectives
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Collecting items according to a
user’s interest
We want to know types of recommendation
algorithm with good performance under
diversified usage objectives
Assumption of the existing recommender systems
Unexpected user behavior
Collecting items that match a certain
theme or to show others
Sports carApple
Travel
Plan
Birthday
ideas

9. Information Available in Pinterest
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1. Content information (description)
→ Content-based filtering
2. User – item pinning information
→ Collaborative filtering
3. Follow relationship
→ Social network-based method
4. # Repin and # Like
→ Popularity-based method
Compare the algorithms to find the best one
Information types and algorithm types to be applied

10. 1. Compare the major recommendation methods
To know which type of algorithm (and differ in
information types) works the best
2. Evaluate the results according to accuracy and
usefulness
To understand the result in various aspects
Objective
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