Movie Prose - A Business Intelligence system


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Datamining in Business Intelligence - course project

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  • Movie Prose - A Business Intelligence system

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