Zhenfei Feng: The Impact of Social Influence on Users’ Ratings of Movies

Presented by Zhenfei Feng
Supervised by Pr. Laurence Favier
GERiiCO Research Lab.
University of Lille Humain and social Sciences
Presented at The 4th International Scientific Conference
Information Science in the Age of Change
Innovative Information Services
15 May 2017
Presented by Zhenfei Feng
Supervised by Pr. Laurence Favier
GERiiCO Research Lab.
University of Lille Humain and social Sciences
Presented at The 4th International Scientific Conference
Information Science in the Age of Change
Innovative Information Services
15 May 2017
THE IMPACT OF SOCIAL INFLUENCE
ON USERS’ RATINGS OF MOVIES

Part II: Research Questions
Part III: Methodology
Presentation ContentsPresentation Contents
Introduction
Part IV: Results and Discussion
Part I: State of art
Part V: Conclusion and Further Research

INTRODUCTIONINTRODUCTION
Since 1990s, Recommender systems (RS) have become an
important research area which attracts attention of many
researchers. Several approaches have been proposed and many
algorithms have been developed.
RSs have also been widely applied in various industry domains
to help users select items such as movies, music, news articles,
Web pages, etc.

In movie recommender area, RSs are used to provide
personalized movie recommendation services. Typical
applications are MovieLens and Netflix.
Part I: State of artPart I: State of art

They infer users’ preferences by asking them to rate
movies on 1-5 point or 1-10 point rating scale ranging
from “Strongly Dislike” to “Strongly Like”.
Then, they recommend to users movies which match up
with their personal preferences.

This mechanism works well in these sites for the reason that:
• users are nearly independent and are only implicitly related
to each other through their co-rated movies.
• users are hardly influenced by others’ opinions and they rate
movies only by their own preferences.
Thus, their ratings reflect more precisely their movies preferences.

RSs are also largely applied in online movie review sites and
social networks for movie fans such as IMDb, Flixster, Rotten
Tomatoes, Allociné, and Douban.
In these applications, however, users are more or less
connected to others. Thus, social influence occurs and may
change users’ rating behaviors and their ratings.

In this case, users’ ratings might not be reliable enough to infer users’
preferences for the reasons that:
1. Users might also take movie quality into consideration besides their
own preferences while rating movies.
2. Users’ ratings seem to be more likely influenced by others’ ratings.
Social influence can be defined as change in a person's cognition, attitude,
or behavior that results from interaction with another individual or a group.

These sites aim at helping users infer movie
quality and their rating scales are ranging from
“Awful” to “Excellent”, as they explain to their
users.
It looks like that users are expected to
rate movies more objectively by
movies’ quality

In this case, users’ ratings might not be reliable enough to infer users’
preferences for the reasons that:
1. Users might also take movie quality into consideration besides their
own preferences while rating movies.
2. Users’ ratings are more likely to be influenced by others’ ratings.

In these sites, users are connected to each other. They can not only
express their opinions by voting for movies and posting reviews, but
also to learn others’ opinions.
Users are more likely to be influenced by others’ opinions, especially
when they disagree with others.

Part II: Research QuestionsPart II: Research Questions
Here come our research questions in this studies
1. How exactly users rate movies in these sites
a. Do they have different criteria to rate movies?
b. Which criterion do they use while rating movies?
2. Are users influenced by previous ratings when they disagree with others’
opinions?
a. Are they influenced by the average ratings?
b. Are they influenced by their friends’ ratings?
c. Are they influenced by the film critics’ ratings?

H1a. Users may dislike a movie in spite of its good quality perceived.
H1b. Users may love a movie regardless of its poor quality perceived.
We assume that users have two different criteria to rate movies and they
are not always the same, thus we hypothesize:

H2a.Users tend to give a high score to a movie whose quality they
judge “excellent” even though they do not really like this movie.
H2b.Users tend to give a low score to a movie with poor quality
even though they like this movie.
Second, for the users who have 2 different criteria, we suppose that they
care more about movie’s quality, and hypothesize that:

H4. Users will give a higher score to a movie well rated by others although they
have low opinion of this movie.
H5. Users will a give lower score to a movie poorly rated by others although they
have high opinions of this movie.
We examine especially two situations when they disagree with others
2. When users disagree with others’ previous raings, will they be
influenced?

Part III: MethodologyPart III: Methodology
• The primary goal of this study is to examine whether users’ ratings reflect
their real preferences.
• We consider two main factors which are most likely to influence users’
ratings: users’ rating criteria and impact of others’ previous ratings.
• Due to the difficulty to measure users’ attitude and preference, a survey
questionnaire is designed which allow us to understand how exactly users
think while they rate movies.

Part III: MethodologyPart III: Methodology
The platform which we chose for our study is Douban, an online rating
website for books, movies and music in China.
• It provides users’ rating, review and recommendation services for movies,
books and music. Users can rate items on a 5-star ratings scale.
• It also provides Facebook-like social networking services.
Hence, Douban is an ideal source for our research.

Part IV: Results and DiscussionPart IV: Results and Discussion
In total, 2000 questionnaires were randomly sent and 297 participants
completed this survey.
Due to the specificity of the site, we cannot judge the users’ gender
according to their profile and there were more females (201) than males
(96).
In terms of age distribution, most of the participants are in the 18-25 range
(51.5%) and 26-30 (34.3%). Besides, there are fewer participants in the 31-
40 range (11.4%), under 18 (2%) and 41-50 (0.7%).

All the participants have rated at least 10
movies.
The number of movies that they have
rated ranging from 10 to more than 1000.
55.9% of the participants (166) follow
some film critics.

According to the result,
• most of the participants (73.5%) agree with either H1a or H2b, which
suggests that they do feel that they have two different criteria.
It implies that this kind of users might rate differently by using different
criterion.

For the participants who have two different rating criteria, they rate movie
based on
• the movie quality (40.6%) ,
• their own preferences (41.6%) ,
• both the movie quality and their own preferences (17.8%) .
For H2a, only 1.8% of the participants reported that they gave low scores to the
movies that they didn’t like in spite of the “excellent” quality they had
perceived.
As for H2b, 54.3% of the participants claimed that they gave high scores to
movies that they loved despite the poor quality they had perceived.

In summary,
• most participants feel like that they have two different rating criteria and
while they rate movies, they may use different criterion.
• The result shows that, besides movies that they love, users tend to also give
high scores to the movies with high perceived quality regardless of their
preferences.
• Nevertheless, the standards of perceived movie quality differ from each
other, it would be difficult to determine which criterion they use while
rating movies.

Most of the participants reported that they felt hesitant when their opinions
disagree
• with the majority (73.8%),
• with their friends (70.1%),
• and especially with the film critics they follow (78.4%).
This suggests that previous ratings may have chance to influence users’ ratings.
2. When users disagree with others’ previous ratings, will they be
influenced?

For movies to which participants had intended to give low scores,
• 73.8% of them, gave higher scores to the movies whose average
rating were high.
• 73.5% of them, gave higher scores to the movies that were highly
rated by their friends.
• 77.8% of them, gave higher scores to the movies that were highly
rated by film critics.
It suggests that these 3 types of positive ratings influence most users

As for movies to which participants had intended to give high scores,
• 62.8% of them, gave lower scores to the movies whose average
rating were low,
• 62.7% of them, gave lower scores to the movies that were poorly
rated by their friends
• 71.3% of them, gave lower scores to the movies that are poorly rated
by film critics they follow (71.3%).
Thus, negative ratings also influence most users.

According to these results, these three types of previous ratings all influence
most of the participants.
• the film critics’ opinions influence relatively more users than the average
ratings, and the friends’ opinions influence relatively fewer users.
• Positive ratings have an impact on more users than negative ratings.
It suggests that users are not only influenced before they watch movies, but most
users are also influenced after watching movies. Thus, users’ ratings may not
reflect exactly their real opinions.

Part V: ConclusionPart V: Conclusion
Our analysis suggests that:
1. most users have two rating criteria and that they may use different
criterion to rate different movies;
2. most users are more or less influenced by others’ ratings even when they
have already their own opinions.

Zhenfei Feng: The Impact of Social Influence on Users’ Ratings of Movies

Zhenfei Feng: The Impact of Social Influence on Users’ Ratings of Movies

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