The role of social connections in shaping our preferencesUnderstanding sharing and consumption online

Amit SharmaPh.D. Candidate

Dept. of Computer ScienceCornell University

www.cs.cornell.edu/~asharma@amt_shrma

Collaborators

● Dan Cosley, Advisor, Cornell University● Baoshi Yan, LinkedIn● Gueorgi Kossinets, Google● Jake Hofman and Duncan Watts, Microsoft Research● Students

■ Masters: Meethu Malu, Mevlana Gemici, Michael Triche

■ Undergraduate: Yulan Miao

Finding meaning in social data

People express their connection with items in myriad ways on the web

Examples● Hashtag on Twitter● Like on Facebook● Rate on Goodreads● +1 on Google● Favorite on Etsy

How do these activities connect to people’s decisions on items, products, opinions?

Connection between retweeting and influence, liking and buying, sharing and consuming

How items diffuse through social networks: The “holy grail”

Past work has studied:● intrinsic attributes of

items● the influence of certain

individuals

Selection Bias: Studies on online data show that most shares propagate only one level; a tiny fraction get to more than 1 level

Sharing + Independent Consumption across the network

Sharing + Independent Consumption across the network

Sharing + Independent Consumption across the network

Ego Networks

● Can our friends’ activities be used to predict ours?

■ You like this because Jeetu liked it.● Can information about our friends’ activities

help us make decisions on items, form our opinions?

■ Amit Sharma and 10 of your friends like this.● Would our friends suggest items that we

would like?■ Jeff Bezos shared this to you.

Three questions for research

Three questions for research

Can our friends’ activities be used to predict ours?

Can information about our friends’ activities help us make decisions on items, form our opinions?

Would our friends suggest items that we would like?

How to design network-aware recommendation models?

How to present social information in system interfaces? dummy

How to include manual shares in recommender systems?

Ego NetworkSubgraph containing a person and her immediate social connections.

FriendAny first-degree connection of a person as defined by a particular social network.

Preference(Partial) ordering over items that helps a person choose items to consume.

Definitions

Part I: Predicting users’ activities based on friends’ activitiesA study using data from Facebook and Twitter

ICWSM 2013

Datasets from Facebook and Twitter

Preferences: Movie and music Likes on Facebook, hashtag usage on Twitter

Data collected from people who gave permission to Facebook apps [Sharma and Cosley 2013b, McAuley and Leskovec 2012]

What would be good measures of preference locality?

● User similarity-based: how similar are people’s activities on items in the ego network versus the full network○ Similarity between users○ Density of the user-item matrix

● Item coverage-based: how widely spread are items in the network○ Number of ego networks an item is a part of○ Comparison with random graphs

User A : [Titanic, Braveheart]User B: [Braveheart, Star Wars]User C: [Star Wars, Star Trek]

Similarity: Jaccard similarity

Sim(A,B) = ⅓ Sim(A,C) = 0 Sim(B,C) = ⅓

Measures of locality: Similarity

Measures of locality: Sparsity

Measured by the density of the user-item matrix

Density = 6/12 = 0.5

Titanic Braveheart Star Wars Star Trek

User A 1 1 - -

User B - 1 1 -

User C - - 1 1

Evidence of locality for all three domains.

Hashtags show higher locality than artists or movies on Facebook.

Measures of locality: Item coverage

Uncovered Ego: Percentage of ego networks that do not contain a given item.Random Item/Ego: Compare uncovered ego of given network with a network constructed by randomizing the item likes between users.Random Friend/Ego: Compare uncovered ego of given network with a network constructed by randomizing a user’s friends.

Hashtags have highest locality.The metrics are divided between artists and movies on Facebook.

Similar locality results for item coverage-based metrics

So far, we have seen aggregate metrics.

How does locality perform on predicting each user’s preference?Consider a 70-30 split between train and test.Two sets of data:● One using only friends (Friends / Local)● One using whole network except friends (Non-Friends /

Global)Algorithms: k-nn, matrix factorizationEvaluated on NDCG metric, widely used in IR and recommender systems.

k-nn similarity is higher for non-friends than friends

k-nn recommender based on friends outperforms or is comparable to those using non-friends

Number of friends ~100-500

Number of non-friends ~50k

NDCG for 50-nn using friends, non-friends and the full network. Recommendations from friends are are still comparable to those from the full network.

Typical use case: Using friends + non-friends

Useful for recommender systems are exposed only egocentric slices of the network (e.g. through third party APIs of Facebook and Twitter)

Part II: How social processes work to influence our preferencesA specific example: Social explanations on the web

WWW 2013

A specific influence process: Social explanations for a recommendation.

A specific influence process: Social explanations for a recommendation.

Amit Sharma rated it 5/5!

How explanation strategies serve as proxies for social processes

Overall Popularity (OVP)

Social Process: Proof

Count of Friends(CFR)

How explanation strategies serve as proxies for social processes

Social Process: Conformity

Social Process: Influence

Random Friend(RFR)orGood Friend(GFR)

How explanation strategies serve as proxies for social processes

Good Friend & Count(GCFR)

Social Process: Conformity and Influence

How explanation strategies serve as proxies for social processes

A user study (N = 237)

● Within-subjects design.● Musical artists recommendation. Chose

artists which users were not aware of.● Participants were exposed to

recommendation accompanied by different explanation strategies.

● Each participant rated a maximum of 30 recommendations.

● At the end, participants also answered a questionnaire.

Example Interface

Pink Floyd

+Social

Explanation

How likely are you to check out this artist?

Likelihood Rating (0-10 Likert)

=

PHASE I

Different strategies lead to different ratings

More insights into people's rating decisions

Showing the right friend matters

Popularity matters only if people identify with the crowd

People are differently susceptible to explanation

“I found it most powerful when I could see what friend likes the artist. I know what kind of music my friends listen to and that helps me know if I would like the artist or not."

“If it was a friend thatI did not think I would have similarly music taste too, thenI immediately ruled the artist out...”

"The recommendations that were most convincing to me were the ones thatdisplayed that a decent number of my friends listened to orliked the artist. I often like to hear my friends’ feedback oncertain artists..."

"Me and my friends’ music tastes rarely match up, so I’ve learned to not care about what music my friends like."

More insights into people's rating decisions

Showing the right friend matters

Popularity matters only if people identify with the crowd

People are differently susceptible to explanation

More insights into people's rating decisions

Showing the right friend matters

Popularity matters only if people identify with the crowd

People are differently susceptible to explanation

More insights into people's rating decisions

Showing the right friend matters

Popularity matters only if people identify with the crowd

People are differently susceptible to explanation

Social explanation is a secondary effect

"The albums with the most interesting picture, or interesting name, with a lot of likes. If the name struck me, such as ‘Formidable Joy’, I found myself wondering more.If a lot of my friends liked it, it must be good!"

Based on a combination of these two decision processes, a user evaluates a recommendation.

Pink Floyd User's receptiveness to an explanation.[Effect of Explanation]

User's discernment in music.[Base Decision Process]

Amit Sharma likes Pink Floyd.

Modeling the effect of explanations

Base Decision Process f(x) = A e-Ax

A generative process of influence for explanations

A: Discernment

Base Decision Process f(x) = A e-Ax

A generative process of influence for explanations

A: Discernment

Effect of Explanations mu : Receptivity

sigma: Variability

Base Decision Process f(x) = A e-Ax

A generative process of influence for explanations

A: Discernment

Effect of Explanations

Mixture Model h(x) = a f(x) + (1-a) g(x) a : Rigidness

mu : Receptivity

sigma: Variability

Good Friend strategies show lowest rigidness.

Why is this a likely model? All models show same discernment ~0.4

● The effect of social explanation varies with different strategies and different people. Can be used for personalized explanations.

● Explicitly named friends (influence) more impactful than count of friends (conformity).

● Still, aggregate effects can be modelled. A generative model gives us a window into people’s decision process.

Findings

Part III: How people choose items to shareRole of own versus others’ preferences

Sharing is common, and often directed to individuals.

What is the role of people’s preferences in sharing?

Past research shows that when broadcasting, people tend to share only highly liked items [Sharma and Cosley ‘11, Naaman et al. ‘10]A lot of sharing still directed at specific people. How do people choose items to share directly with a recipient?● Altruism suggests that people will share what they

expect the recipient to like● Individuation suggests that people will share what they

like themselves

Where does the balance lie, and how can we model it?

A paired study (N=87 pairs)

● Facebook users invite a friend to take part in the study

● See identical recommendations sourced from each users’ movie Likes

● Recommended can be rated and/or shared with the partner

● To control for social influence, users do not know which items were shared to them

Three groups of participants

● Both_shown: Pairs who saw a mix of recommendations personalized on both partners’ Likes○ Own_algo: Personalized for partner A○ Other_algo: Personalized for partner B

● Own_shown: People who saw only recommendations personalized for them

● Other_shown: People who saw only recommendations personalized for their partner

Partners of Own_shown are in Other_shown and vice-versa.

Shared items are rated higher by senders than non-shared items

Senders have higher ratings for shared items than recipients

Using people’s preferences for predicting shares

Individuation seems to dominate, but still participants claimed they were personalizing for the recipient

“Usually when I suggest, it depends on the item, notthe target individual, because I want to share what I enjoyed.” [P8]

“I make suggestions to people if I think they might gainenjoyment. Obviously it really depends on their personalityand their likes/dislikes.” [P22]

Preference-Salience model of sharing

People do not really try to balance individuation and altruism when they share items. Rather, they share based on their preference for items and what is salient to them at the moment. Recipient help decide whether to share an item or not.

Alternative hypotheses:High Quality Sharers: Shared items not significantly higher rated on IMDB than non-shared items.Misguided Altruists: Shared items have consistently higher rating by the senders.

People’s decisions on items depend on both preferences and social factors.

Requires mixed methods approach (Data mining + online experiments).

Models of people’s decision processes can predict what items are more likely to be adopted or shared.

thank you

Amit SharmaDept. of Computer Science

Cornell Universityhttp://www.cs.cornell.edu/~asharma/

@amt_shrma