Who Should Share What?
Item-level Social Ifluence
Prediction for Users and Posts Ranking
RunquanXie
E-mail: RunquanXie@126.com
厦门大学计算机科学系
2011年11月
2015/4/13
Author information
Peng Cui Research Assistant
Shaowei Liu Ph.D.
Shiqiang Yang Professor
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ABSTRACT
1.Two core dimensions in a social network：information people
basic behavior：people sharing information spread
2. Who Should Share What?（predicting item-level social influenece）
Two information retrieval scenarios
(1) Users ranking an post
(2) Posts ranking an user
3. Formulate this problem : the estimation of a user-post matriX, each
entry : the strength of influence of a user given a web post.
4. Hybrid Factor Non-Negative Matrix Factorization approach
Devise an efficient projected gradient method
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INTRODUCTION
•With the rapid proliferation : Facebook, Renren etc., more and
more user profiles,interactions, and collective intelligence ( tags,
comments, etc.) are available online.
•a new perspective for information retrieval applications:
• more focus should put on user collaborative information.
•MeanWhile, new search scenarios:
•web people search and relationship search emerged.
•challenge to traditional information retrieval:
• how to effectively handle the social information?
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INTRODUCTION
1.A key concept : Social influence a prevalent [普遍]and complex
force governing the dynamics of social network.
2.a clear need for techniques to analyze social influence and this issue in
information retrieval field has still not been well studied.
3. The existing social influence analysis research[summarized into a diagram]
Who(A) influences Whom(B) given What(C)?
A:a single user
B:the whole network or a single user or the A’s friends (neighborhoods) in
different previous works
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INTRODUCTION
C : three lines of research
1.Structure-level [C is empty]
2.Topic-level [C is a topic]
3.Item-level [C is an item (such as a web
page, product etc.)] rare in previous
research.
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Motivating Application
Definition on Wiki : social influence occurs when an
individual’s thoughts, feelings or actions are
affected by other people.
In Renren, when a user share a web post, a portion
of her friends will click, comment, or even forward
the post, which are three levels of influence . In this
paper, we only consider click action. That is, the
social influence of a user on her friends given a
web post is defined as the number of her friends
who click the shared web post.
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Challenges
1.User-post specific
User-post matrix
2. Sparsity
effective prior knowledge for user and post grouping to alleviate the
sparsity problem.
User-user similarity matrix and post topic distribution matrix
3.Complex factors.
For example, the total number of friends, the tie strength between the
user and her friends, the semantics of web posts, etc., How to select the
effective factors and integrate these complex factors in one predictive
model is also one of the focus of our work.
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Contributions
1. Formulates the item-level social influence prediction problem formally
with HF-NMF, and devise an efficient projected gradient method to
solve it.
2. Support the applications such as influencer ranking and information
recommendation by user-post matrix ranking in two directions.
3. social influence extendable to the influence on all the friends and the
friends of friends etc.
4. We conducted intensive experiments on real social network datasets,
and the results show that the HF-NMF can achieve a better
performance compared with other competitors.
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PRELIMINARIES : Necessity of Item-level Social Influence
1.The dataset http://renren.com/, 150 million active users.
2.A user can generate a post or share a web page and as
the number of friends is different for each user, the upper
bound of users’ social influence strengths are also
different. In order to make the strength of influence be
measured in a unified scale for different users for the
sake of observational and modeling study, we use the
proportion of friends who click the shared post as the
measure.
3.Figure 1
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Figure1
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Figure1
(1) different users have different influence power to
their friends
(2) different posts have different influence power
(3) users’ influences differently for different posts
Conclusion :
Only item-level social influence can reveal the
users’ real influence on friends, and the strength
of influence should definitely be user-post specific.
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Predictive Factors
1. User-specific factors.
Although users’ social influence vary with the shared posts, the average of
the social influences over posts determines the overall social influence
of a user. We regard the factors that affect users’ overall social influence
(excluding the posts) as he user-specific factors.
2. Post-specific factors.
posts’ overall social influence
3. User-post specific factors.
The social influence of a user given a post cannot be well approximates
only by the user and post-specific factors. The factors indicating the
interactions between users and posts.
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Predictive Factors
emphasizing
1.The user and post factors are essential for the predictive modeling.
2. The user-post interactions are very sparse.
alleviate : find effective factors to “group” those users and posts
3.The user and post-specific factors also provide some effective prior
knowledge to complement the inference from pure user-post
interactions.
4. How to find out the effective predictive factors??
Finally : Two user-oriented factors: the percentage of active friends, the
average social tie strength between a user and her friends,
One post-specific factor: the topic distribution of a post’s content.
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demonstrate the validity of selected factors
Randomly select 10 users.
Define user social influence : the average percentage
of her friends who click the shared post over all
shared posts.
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Post topic distribution
Hypothesis : the posts with similar contents ( similar topic distributions)
often induce similar social influences.
Validate: we randomly select ten groups of web posts, and posts in the
same group have similar topic distributions.
Define web post social influence same with above. Then calculate and plot
Conclusion :
1.most variances inside topic groups are smaller than that across groups
2.The introduction of topical grouping :reduce down the uncertainty of social
influence.
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ALGORITHM
1.Suppose :M users ui and N postings pj.
N(ui) : ui ’s first-order friends.
2. Item-level social influence
the strength of ui’s influence on N(ui) : fij, is the
number of ui’s friends whO clicked pj .
3. Social influence prediction[M*N]
The social influence prediction is to predict the
unobserved social influences fˆij based on the
observed fij ’s and those predictive factors.
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Formally
X ∈ RM×N
Gi : the number of ui’s friends
different users have different numbers of
friends
Problem: predicting the unobserved entries in X.
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Hybrid Factor Non-negative Matrix Factorization
• Suppose:a joint latent space for both users and posts
with dimensionality k
• Ui : an user vector ui ∈ Rk
• Pj : a post vector vj ∈ Rk
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(HF-NMF)
Focus more on the valid[有效] elements
Sharing matrix :
The severe sparsity of X??
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Incorporating User-Specific Factors
1.The percentage of active friends:
2. the average friend tie strength:
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1.the similarity between ui and uj
2. the user-user similarity matrix
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Incorporating Post-Specific Factors
1.The post content matrix
social influence is strongly correlated with the content of the web posts
2. Finally
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Solution
1.a nonnegativity projection operator P[ ] formatrix A
Problem:
Variants of projected gradient methods differ on selecting the step size
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Algorithm 1 Projected Gradient
Ensures the sufficient decrease of the function value per iteration.
• By trying the step sizes
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Algorithm 2 Improved Projected Gratient
1. Searching
is the most time consuming operation
2.
.The trick:
and guess
either increases or decreases
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Algorithm 2 Improved Projected Gratient
• a better initial guess of at each iteration and allows to be larger
than one.
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交替下降
=> for
fix V G update U
fix U G update V
fix U V update G
end for
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EXPERIMENTS
1.DataSet [ at least 6 days before the crawling date ]
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Comparative Methods
1.
2.
3.
4.
Logistic Regression
Cox Proportional Hazards Regression
User Averaging Influence
Post Averaging Influence
5. Basic Non-NegativeMatrix Factorization
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Comparative Methods
6.User Factors Constrained NMF
7. Post Factors Constrained NMF
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Evaluation Measures
1.
2. T-measure
R-measure
1 : perfect ranking
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Parameter Settings : Tradeoff Parameters
α =0.0001, β = γ = δ = 0.01
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Parameter Settings : Dimensionality of the Hidden Space
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Number of Iterations
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Prediction Performance
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User and Post Ranking Performance
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Comparison with Other Methods
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CONCLUSION
1.Hybrid Factor Non-Negative Matrix
Factorization method to incorporate these
predictive factors for user-post specific
social influence prediction
2. devise an efficient Projected Gradient
method for HFNMF solution.
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Department of Computer Science, Xiamen University, Nov, 2011
2015/4/13