This document summarizes a research platform called PopCore for conducting experiments on social recommendation. PopCore is based on Facebook data from over 500 core users and allows testing recommendation algorithms and hypotheses on a networked group of people. Previous experiments on PopCore have found that recommendations from a user's first-degree social connections are nearly as good as those from the whole network, and that an item's popularity within a user's ego network is more indicative of their preference than overall popularity. The document proposes a new study on PopCore to compare algorithmic recommendations to people-to-people suggestions.

1. A Research Platform for
Recommendation within
Social Networks
Amit Sharma, Cornell University
www.cs.cornell.edu/~asharma
@amt_shrma

2. The connection between explicit
social networks and preferences

3. Algorithmic gains with social data
vary
● Augment collaborative filtering algorithms [e.
g. Konstas et al. 09, Ma et al. 11]
● Use only first-degree connections [e.g. Guy
et al. 09, Kulkarni et al. 13]
● Gains with social data often slight, varies
with domain [Arazy et al. 10, Groh 12,
Sharma and Cosley 13b]

4. Understanding how preferences
evolve in a social network
● Interactions, not single actors, may be the
key to understanding and predicting
preferences in social contexts.
○ Influence
○ Trust
○ Identity
● Analyzing preferences of single users
situated in a network gives us one part of the
picture. Interactions between users can
give us the other part.

5. Designing and evaluating networkcentric recommenders requires
more than one user
Two general questions:
● How people evaluate recommendations from
their social network?
● How people share items with each other?
One good way is to test our algorithms and
hypotheses on a network of people.

6. PopCore: A research platform
● Based on Facebook
● Supports
○ Recommendation Algorithms
○ Directed Suggestions
○ Network visualizations wrt. preferences
● ~500 core users, 130K users, 2.3M likes

7. Conducting experiments on social
recommendation
● Popularity of an item within first-level ego network
more indicative of preference than overall popularity
[Sharma 11]
● Explicit social explanations have only a secondary
effect on people’s preferences, compared to their
own expectation of liking [Sharma 13a]
● For Facebook and Twitter, recommendations from
social connections are nearly as good as those
computed from the whole network [Sharma 13b]

8. Example study: How can we utilize
people-to-people suggestions?
● People share items to each other often. How
do these suggestions compare to algorithmic
recommendations?
● Goal: Study how people make
recommendations to each other
● Experiment: Ask users to rate and suggest
items to each other, from a set of algorithmic
recommendations

9. DEMO
http://www.popcore.me
http://labs.popcore.me

10. Opening up the platform
● Conducting more experiments
○ Collaborating
○ Repeatable experiments
○ Best ways to collaborate?
● Accessing data collected
○ Subject to data privacy restrictions
thank you
Amit Sharma
http://www.cs.cornell.edu/~asharma
@amt_shrma