This document presents research on exploring social influence through word-of-mouth recommendations. The study finds that word-of-mouth recommendations can significantly increase users' posterior evaluations of recommended products or services. Two models of this phenomenon are proposed and tested statistically. The results support the conclusion that recommendations directly influence higher ratings rather than common unknown factors explaining both. The paper also develops a method to identify influential friends based on their social positions and characteristics.

1. Exploring Social
Influence via
Posterior Effect of
Word-of-Mouth
Recommendatio
ns

2. ABSTRACT
 the
posterior effect of
word-of-mouth recommendations：
whether or not word-of-mouth
recommendations can influence users’
posterior evaluation on the products or
services recommended to them.
 word-of-mouth recommendations
directly prompt the posterior evaluation of
receivers.
 a method for investigating users’ social
influence.

3. CONTRIBUTION




This paper finds a counter-intuitive phenomenon
that word-of-mouth recommendations are
correlated to a raise in users’ posterior evaluation.
We prove, for the first time, that word-of-mouth
recommendations can significantly prompt users’
posterior evaluation.
We propose a framework to quantitatively
measure individuals’ social influence by examining
the number of their followers and their sensitivity of
discovering good items.
We develop a method for identifying influential
friends with strong social influence.

4. PRELIMINARY STUDY
1.
Data collection

Douban and Goodreads

5. If a user rates an item that has been recommended by anyone
he/she follows, we consider it as a rating with word-of-mouth
recommendation. Otherwise, it is a rating without word-of-mouth
recommendation .

6. 2. Higher ratings on items with
recommendations

7. an individual is more likely to present
a high rating to an item with a wordof-mouth
recommendation, compared with an
item without.
a word-of-mouth recommendation is
correlated to a raise in user posterior
evaluation .

8. an item recommended at least
once has a much larger
collection count than an item
without any
recommendation, and the
collection count of an item is
strongly related to its
recommendation count.
the mean rating of items with at
least one recommendation is
higher than that of items
without recommendations.

9. POSTERIOR EVALUATION MODELS
 two
1.
2.
possible models :
a word-of-mouth recommendation and
a higher rating of an item both attribute
to certain common but unknown factors;
a word-of-mouth recommendation
directly influences the rater to present a
higher rating.

10. MODEL VERIFICATION
1.
Designing statistical hypothesis tests
recommender’s rating, r’
a) r’ depends on c symmetrically as r in the
original independent model;
b) m’ fully depends on r’ due to a common
practice that whether or not a user
recommends an item is determined by
his/her rating on this item.


examining the conditional independence
between m’ and r given r’

11.  we
discretize the real-valued r’ into
distinct values in two ways:


For the equal-interval partitioning, the
values of r’ in each partition are confined
within a fixed small interval.
For the equal-frequencies
partitioning, each partition has a fixed
number of data points.
 p(m’|r’)
and p(r|r’)
 p(r|m’=0,r’)
p(r|m’=1,r’)
 The
location t -test and the KolmogorovSmirnov test

12. 2. Results of statistical hypothesis tests

13. 
1. Slice data into partitions. All data points in
each partition share a unique r’ value.

2. Divide each partition into two samples, one
with m’= 1 and the other with m’=0.

3. In each partition, employ the two-sample t
-test and a two-sample Kolmogorov-Smirnov
test to examine the null hypothesis, i.e. the
two samples are identically distributed.

4. Report the percentage of partitions where
statistical hypothesis tests reject the null
hypothesis.

14. IDENTIFYING INFLUENTIAL FRIENDS
 Two
kinds of factors, namely, the social
positions of users in the friendship network and
their personal characteristics independent on
other individuals.
1. PageRank and LeaderRank
2. collection size, collection frequency (number
of items collected per day) and sensitivity.

16. It is necessary to combine the social position of individuals
and their personal characteristics when identifying the
influential friends for social recommendations.

17. CONCLUSIONS
 word-of-mouth
recommendations can
not only improve the prior expectation of
users, who receive the
recommendations, on the recommended
items, but also raise their posterior
evaluation.
 a method for investigating a user’s social
influence.

18. Maximizing Product Adoption in
Social Networks

19. ABSTRACT
 it
is important to distinguish product adoption
from influence.
 We adapt the classical Linear Threshold (L T)
propagation model by defining an objective
function that explicitly captures product
adoption, as opposed to influence.

20.  maximize
the number of product adoptions
 contributions :
1. We present an intuitive model called LT-C , for
LT with Colors
2. We study two types of networks movies
networks and music networks
3. we found that there is a positive correlation
between the number of initiators and the final
spread.

21. PROPOSED FRAMEWORK
G
=(V;E;W)
 a matrix R of Users * Products
1. LT -C Model

22. 2. Maximizing Product Adoption

24.  3.


 4.



Choosing Optimal Seed Set
Monte Carlo simulation
CELF algorithm
Learning Model Parameters
Edge Weights
Ratings Matrix
Node Parameters λ and μ

25. MODEL EVALUATION


Classical LT. Linear Threshold model.
LT-C. Our proposed model.
LT Ratings. Our proposed model without Tattle
nodes. That is, all the nodes who are influenced
adopt the product.
LT Tattle. Our proposed model without any ratings.
That is, all the nodes in Adopt state are assumed
to rate the item as r-max ,as do those in Promote
state, while users in Inhibit state rate r-min.

26. 1. Adoption of Movies

29. 2. Adoption of Music

30. CONCLUSIONS
a
propagation model called LT-C model
 We formalized the problem of adoption
maximization as distinguished from
influence maximization and showed that
it is NP-hard.

31. Thanks