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Movie Prose - A Business Intelligence system
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Movie Prose - A Business Intelligence system

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Datamining in Business Intelligence - course project

Datamining in Business Intelligence - course project


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  • Transcript

    • 1. Movie Prose Using B.I. for Movie Recommendations and Customized Ads Ankur Kath, Arun Agrahri , , Avanidhar Chandrasekaran ,Jou Vang, Ted Armstrong, Tyson Burke
    • 2. Outline
      • Introduction
        • Project/Problem
        • Available Data . Demographic profile of users
      • How our system works?
      • Benefits of Recommendations
      • Related Systems
      • Approaches
        • Content –based
        • Collaborative
        • Hybrid
      • Analysis
        • Methods
        • Challenges
        • Outcomes
        • Conclusion
    • 3. Recommendation Engine ( System Design)
    • 4. System Components
      • Profile Manager
      • Collect information based on user activities
      • Construct, re-construct the profiles
      • Recommendation System
      • Recommends movies to new and registered users
      • Provides FEEDBACK
    • 5. Benefits of Recommendations
      • Personalization
      • Can recommend movies to “similar” users
      • Feedback to the Profile Manager
      • Correlations among the profiles (can use classification techniques, affinity analysis)
      • Business purposes
      • Targeted Advertisements
      • Movie makers can use the trends to do a better business
    • 6. Approaches
      • Content-Based
        • Seeker’s Preferences, based on machine learning algorithms
      • Collaborative
        • Preference matching and weighting techniques
      • Social Data Mining
        • Mines preferences : seeker’s preference typically not used
        • Data Mining Techniques
    • 7. Related Systems
    • 8.  
    • 9. Analysis
      • Prediction Model
      MovieLens DataBase Training Data Classification Model Profile Manager Prediction Model
    • 10. Demographic Profile Age Gender Occupation Genres 20 Male student Adventure, Fantasy, Children 21 Female student Fantasy, Sci-Fi, Children 32 Male Engineer Adventure, Action 33 Female Engineer Adventure, Sci-Fi, Action 34 Male Doctor Sci-Fi, War, Action 35 Female Doctor Adventure, Sci-Fi, Action 31 Female Technician Mystery, Sci-Fi, Thriller 36 Male Technician Action, Adventure, Romance, War, Sci-Fi 28 Male Entertainment Adventure, Sci-Fi, War, Action 27 Female Entertainment Adventure, Comedy, Action 32 Female Marketing Action, Adventure, Romance, Sci-Fi, War 37 Male Marketing Sci-Fi, Thriller, Action
    • 11. AGE GENDER Occupation Movie Recommendations Recommender Engine User Preference Inferences
    • 12. Personalized Movie Advertisements to Targeted users
      • Current approach to Advertisements
      • Rely on spamming to let users know about a new movie.
        • Use : Large user base to spam to
        • Drawbacks : Leads to user frustration
      • Our System
      • Advertises movies to users based on user profile.
      • Uses data mining techniques to determine the best movies to recommend to users.
    • 13. Rating Occupation Movie Recommendations and Advertisements Recommender Engine/ Affinity Analysis Genre Specific User Rating Genre Movie Name
    • 14. Average Rating for Genres
      • GOAL:
        • Determine relation between user occupation and average rating for each genre
      • PROCESS:
        • Determine the average rating each user has for each genre
        • Preprocess results;
          • Deleted NULL values
          • Associate Occupation with average rating for each Genre, e.g. Student => 4.5 for Action
        • Use affinity analysis to develop association rules
    • 15.
      • RESULTS:
        • Rules relating Occupation to Average rating per genre.
        • These rules can be used to advertise select movies to users.
        • Limitations:
        • Results may vary based on age too.
        • eg) A young student could rate action highly whereas an old student would rate comedy highly.
    • 16. Highly Rated Movies
      • GOAL:
        • Determine association rules among highly rated movies
      • PROCESS:
        • Query movies rated over 4 for each user
        • Use affinity analysis to derive associations among highly rated movies.
    • 17. Highly Rated Movies
      • Results:
        • Set of rules associating Highly rated movies from different genres.
        • If highly rated movies of Genre A andB are associated with a highly rated movie of genre C, a user who rates movies of Genre A and B highly can be recommended a movie of Genre C with a high confidence of it being rated high.
        • Eg) Face off_4.5, Babel_5 => Sweeny Todd_4.
      • Limitations of the Approach:
        • Very popular movie will affect the results.
        • Eg) Matrix, Star Wars are always rated high and are not statistically significant
    • 18. Implications of our Approach
      • USER:
          • Better and more accurate recommendations
          • Reduced load. Does not have to sift through tons of data
      • BUSINESS:
          • Improved user satisfaction -> Increased revenues.
          • Convert occasional browsers to potential users.
    • 19.
      • Monitor ratings of advertised movie over a period of time
      • If ratings have increased or remained high, the method has worked.
      • Else, rethink personalization policy
      EVALUATION