Date post: | 19-Jul-2015 |
Category: |
Social Media |
Upload: | amit-sharma |
View: | 133 times |
Download: | 0 times |
Modeling the Connection between People’s
Preferences and Content Sharing
Amit Sharma* and Dan Cosley
Cornell University
@amt_shrma
CSCW 2015
Directed sharing: Questions
Why did she share that item?
Does she like it? Will he like it?
Can we predict what items she will share to him?3
Two motivations for sharing
Word-of-mouth
Individuation• Establish a distinct
identity for oneself
Altruism• Help others
[Dempsey et al. 2010]
Online Content sharing
Sender’s preferences• Sender shares what she
likes
Recipient’s preferences• Sender shares what
recipient would like
Comparing sender’s rating versus recipient’s rating for a shared item can indicate the relative effect of these
motivations. 4
Directed sharing: More altruism?
• Meformers versus informers: ~80% of content shared on Twitter was about the user [Naaman et al. 2008]
• In directed sharing, there is a known recipient• Expect altruism to be more important
5
Research questions
• RQ1: To what extent do people tend to share items that they like themselves (individuation) versus those that they perceive to be relevant for the recipient (altruism)?
• RQ2: Can we predict whether an item is shared based on sender’s and recipient’s preferences?
6
Person A’s movie Likes
Compute recs.
Person B’smovie Likes
Compute recs.
Combine recs.
A paired experiment on Facebook
7
10 Recs.
forme
10 Recs.
for partner
To mitigate social influence effects, my partner is not shown which movies were shared by me.
8
Senders rate shared items higher than recipients
Mean sender rating: 4.19Mean recipient rating: 3.88
(Paired t-test)
Sender Rating – Recipient Rating
Fre
qu
en
cy
11
Responses support individuation
“Usually when I suggest, it depends on the item, not the target individual, because I want to share what I enjoyed.” (P8)
“I suggest because I like something and I want to see if other people feel the same way about an item.” (P91)
Altruism:
“I make suggestions to people if I think they might gain enjoyment. Obviously it really depends on their personality and their likes/dislikes.” (P22)
12
Data from people who did not see all recommendations• Due to lack of Like data or API errors.
Recs. forme
Recs. for
partner
Recs. forme
Recs. for
partner
Both-Shown Other-ShownOwn-Shown
13
Ratings for shared items depend on item set shown• Own-Shown: Ratings for shared items by senders
are significantly higher than those by recipients.
• Other-Shown: Ratings for shared items by senders are still high, but recipients ratings are comparable.
• Both-Shown: Same effect when divide items shown to Both-shown participants by the underlying algorithm.
Salience of items impacts what gets shared.
14
A preference-salience model
“I try to assess if the individual that I am recommending to would like the movie that I am suggesting. Otherwise, I do not tell them about the movie, and may think of someone else who would like the movie.” (P5)
People’s own preferences determine shareable items.
Among these candidates, some become salient based on the context.
They are shared if sharer thinks they are suitable for the recipient.
15
Other plausible models
High Quality Model• No difference between overall IMDB ratings for shared
and non-shared movies.
Misguided Altruism Model• Senders’ ratings are higher for shares than non-shares.
16
RQ2: Can we predict what is shared?• Classification task: Given a sender, recipient and an
item, decide whether it was shared or not.
• Features:• IMDB average rating, popularity for item• Recipient’s predicted rating for item• Sender’s predicted rating for item• Sender’s sharing promiscuity
• Randomly sampled an equal number of non-shares. Use 10-fold cross validation and a decision tree classifier.
17
Evaluation metrics
Precision• Percentage of items returned by model that were
actually shared
Recall• Percentage of actually shared items that were returned
by model
18
Better precision with sender-based features
0
10
20
30
40
50
60
70
80
90
IMDB Rating Popularity Recipient-ItemRating
Sender-ItemRating
All
Precision Recall 19
Design implications
Recommender systems for effective sharing• Recommending what to share, who to share it to.
E.g., Feedme system [Bernstein et al. 2010]
Diffusion models with directed sharing• Accounting for sender and recipient preferences
20
Conclusions
• RQ1: Individuation (personal preferences) dominate the decision process for directed content sharing.
• RQ2: Based on sender and recipient preferences, we can (noisily) predict what is shared.
thank you!@amt_shrma
21