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

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V Międzynarodowa Konferencja Naukowa Nauka o informacji (informacja naukowa) w okresie zmian Innowacyjne usługi informacyjne. Wydział Dziennikarstwa, Informacji i Bibliologii Katedra Informatologii, Uniwersytet Warszawski, Warszawa, 15 – 16 maja 2017

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

  1. 1. 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
  2. 2. 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
  3. 3. 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.
  4. 4. 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
  5. 5. 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.
  6. 6. 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. Part I: State of artPart I: State of art
  7. 7. 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. Part I: State of artPart I: State of art
  8. 8. 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. Part I: State of artPart I: State of art 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.
  9. 9. 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 Part I: State of artPart I: State of art
  10. 10. 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. Part I: State of artPart I: State of art
  11. 11. 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 I: State of artPart I: State of art
  12. 12. 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?
  13. 13. Part II: Research QuestionsPart II: Research Questions 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: 1. How exactly users rate movies in these sites a. Do they have different criteria to rate movies?
  14. 14. Part II: Research QuestionsPart II: Research Questions 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: 1. How exactly users rate movies in these sites b. Which criterion do they use while rating movies?
  15. 15. 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?
  16. 16. Part II: Research QuestionsPart II: Research Questions 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?
  17. 17. 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.
  18. 18. 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.
  19. 19. 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%).
  20. 20. Part IV: Results and DiscussionPart IV: Results and Discussion 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.
  21. 21. Part IV: Results and DiscussionPart IV: Results and Discussion 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. 1. How exactly users rate movies in these sites a. Do they have different criteria to rate movies?
  22. 22. Part IV: Results and DiscussionPart IV: Results and Discussion 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. 1. How exactly users rate movies in these sites b. Which criterion do they use while rating movies?
  23. 23. Part IV: Results and DiscussionPart IV: Results and Discussion 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.
  24. 24. Part IV: Results and DiscussionPart IV: Results and Discussion 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?
  25. 25. Part IV: Results and DiscussionPart IV: Results and Discussion 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
  26. 26. Part IV: Results and DiscussionPart IV: Results and Discussion 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.
  27. 27. Part IV: Results and DiscussionPart IV: Results and Discussion 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.
  28. 28. 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.

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