In Twitter, the hashtag #FF, or #FOLLOWFRIDAY, arose as a popular convention for users to create contact recommendations for others. Hitherto, there has not been any quantitative study of the effect of such human-generated recommendations. This paper is the first study of a large-scale corpus of human friendship recommendations based on such hashtags, using a large corpus of recommendations gathered over a 24 week period and involving a set of nearly 6 million users. We show that these explicit recommendations have a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6\% more longevity than other Twitter ties. Finally, we build a supervised system to rank user-generated recommendations, surfacing the most valuable ones with high precision ($0.52$ MAP), and we find that features describing users and the relationships between them, are discriminative for this task.

1. FOLLOW MY FRIENDS THIS FRIDAY!
An Analysis of Human-Generated Friendship Recommendations
Ruth García Gavilanes
Universitat Pompeu Fabra
Neil O´Hare, Yahoo! Research
Luca Maria Aiello, Yahoo! Research
Alejandro Jaimes, Yahoo! Research

2. Twitter: Users tweet recommendations

3. Advantages
Recommendations from friends
• Trust
• More
acceptances( Amin et al., Recsys’12)
• Features unknown for machines

4. Disadvantages
• Noise
• Commercial purpose and Spam
• Not all recommendations are delivered to
or seen by target users

5. Humans
Who-to-Follow Recommendations
We propose a way to measure the effect
of recommendations from humans only

6. In 2010
• The most popular hashtag in 2010 was #followfriday
or #ff in several countries
• Recommending people became trendy
• No personalized recommendations of who to follow

7. What is Follow Friday?
TARGET USER
SOCIAL NETWORK RECOMMENDS
PEOPLE TO
FOLLOW ON FRIDAYS.
follows
Tie Strength
to follow or not to follow?

8. Objective
• Analyze the dynamics of Follow Friday : impact,
effect in time, repetitions and longevity.
• Identify important features in each recommendation
by using a classifier

9. Method
24 weeks
48 snapshots
RECEIVER
RECOMMENDER
Follows
ACCEPTED
RECOMMENDATION
RECOMMENDED
USERS
Who is new?
Follows
?
#followfriday
THURSDAY
FRIDAY
SATURDAY
SUNDAY
MONDAY
93% of follow friday Tweets
snapshot
snapshot

10. Acceptance
Total
Initial set of users
55,000
Receivers
0.60% instance acceptance
21,270
Recommenders
589,844
Recommended Users
3,261,133
Recommendation
Instances
59,055,205
Accepted
Recommendation
Instances
354,687
Most Follow Friday Recommendations
are not taken into account
right away

11. Interactions
Mentions
Acceptance
Rate
Recommender -> Recommendation
0.006
Recommendation <-> Recommender
0.009
Receiver -> Recommender
0.010
Recommender -> Receiver
0.011
Recommender <-> Recommendation
0.012
Receiver -> Recommendation
0.095
Recommendation -> Receiver
0.097
Receiver <-> Recommendation
0.145
Most Follow Friday Recommendations
are not taken into account
right away

12. IMPACT

13. Impact
• We need to compare Follow Friday recommendations
to other models:
• Implicit : Mentions that were not Follow Friday
recommendations
• Unobserved : Follow Friday recommendations of the
future only
#FF

15. Acceptance after n weeks

17. Repetitions & Recommenders

18. Features
USER-BASED
(per user)
RELATION-BASED
(per pair)
•
• Attention
• Followers vs Followees
• Mentions by other users
• Recommendations
• Activity
• Average tweets per
•
day
• New followees
• Accepted
recommendations and
recommenders
• Mentions
Tie Strength
– Mentionss
– Folllow Friday
recommendations
– Previous
acceptances
– Friendship longevity
Similarity
– Words, mentions,
hashtags and urls
– Geolocation
RECOMMENDATION-BASED
(per recommendation)
•
Repetitions
•
Format
– Repeated
recommendations
– Different recommenders
– Day of the week
– Re-tweet or not
– Same tweet
recommendations
– Urls

19. Methodology
• Three methods: Rotation Forest, Linear combination and
random
• Training : week 1 to 16
• Test : week 17 to 23
• Up to 2 weeks to calculate acceptance rate
• Recommendations accepted after two weeks were not
considered in the classifier.
• Balanced set for training
• Goal : accepted recommendations towards the top of
the ranking
• Evaluation with Mean Average Precision

20. Results
Ranking
MAP
Features
MAP
Rotation Forest
0.496
All
0.496
Linear
Combination
0.057
User-based
0.074
Relation-based
0.398
Random
0.037
Recommendationbased
0.062
User + Relation
0.518
User + Format
0.079
Relation + Format
0.379

21. Lessons Learned
• Recommendations derived from Social Networks have an
impact on users decisions
• Social accepted recommendations seems to last longer/
more relevant
• Many broadcasted recommendations are not seen
• Not accepted recommendations can be followed in the
future
• Relation and user based features are better predictors of
tie formation

22. Future Work
• Can we rate recommendations according to
permanence/tenure?
• When should we consider an accepted
recommendation? (never vs. some day)
• User study: Can we build an online recommender of
social recommendations (and so promote
recommendations not seen)?
• Add cultural differences in features, is there an
improvement?

23. THANK YOU
@ruthygarcia