These slides present Movie Recommender, a system which provides movie recommendations based on the information known about the users. These recommendations are done using the analysis of the users' psychological profile, their watching history and the movies scores from other websites. They are based on aggregate similarity calculation. The system uses both collaborative filtering and content filtering (using an approach based on different features of the movies from the database). Although there are similar applications available, they tend to ignore the data specific to the user, which in our opinion is essential for his/her behavior

1. Autor Conducător științific
Universitatea
Politehnica
București
Facultatea de
Automatică și
Calculatoare
Catedra de
Calculatoare
Movie Recommender System Using the
User's Psychological Profile
Costin-Gabriel CHIRU, Vladimir-Nicolae Dinu,
Cătălina Preda and Matei Macri

2. Introduction
• Purpose:
– A system to provide movie recommendations to
different users.
– Recommendations based on:
• the analysis of the users' psychological profile,
• the users' watching history,
• the movies scores from other websites.
– Hybrid system, combining collaboration and
content based filtering.
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3. Recommender Systems History
• 1990: MAFIA (MAil-FIlter-Agent) - agent for filtering emails based
on their content (1st
attempt to do a content-oriented information
filtering);
• 1992: Tapestry - mail system developed at the Xerox, also
supporting collaborative filtering - considered 1st
recommender
system;
• 2003: Amazon - uses Item-to-Item Collaborative Filtering and was
specially designed to scale to millions of customers and products;
• 2003: MovieLens - website suggesting movies to users based on
their preferences - used the information provided by other users
for recommendation;
• 2006 - 2009: Netflix competition – prize 1 mil $ for improving by
10% their recommendation - boosted the interest in recommender
systems  other e-commerce recommendation websites;
• 2010: MovieGen: predict genre & production year based on users'
personal information and their answers to some questions related
to the movies ‘characteristics.
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4. Solution
• Website for movie
recommendation.
• Used data: “Top 100 Greatest
Movies of All Time” from
IMDb:
– Movie title;
– Actors;
– Genre(s);
– IMDb score;
– Other information (director,
short description, writer) that
may be used in the future.
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Movie
Genre
Number of movies
having the genre
Adventure 18
Drama 56
Romance 22
War 9
FilmNoir 6
Comedy 18
SciFi 8
Horror 10
Action 13
Animation 2

5. Recommendation (1)
• Hybrid model with 4 features:
– 2 content-based features: simProfile and simRatings;
– 2 collaborative-based features: simMovies and simKNN.
• In order to compute them, we needed for each
user:
– a history of previously watched movies and
– 4 percentage scores representing the user's
psychological profile.
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7. Profile Characteristics Movie genres
Choleric
extroverted, hot-tempered, quick thinking, active,
practical, strong-willed, self-confident, independent
Horror, War, Action,
Comedy, Romance
Sanguine
extroverted, fun-loving, impulsive, entertaining,
persuasive, easily amused, optimistic, receptive,
animated, excited
Action, Adventure,
Comedy, Drama,
Romance
Phlegmatic
introverted, calm, unemotional, easygoing, slow,
indirect, practical, patient, persistent, consistent
Sci-Fi, Tragedy, Cult,
Drama, Film-Noir
Melancholic
introverted, logical, analytical, factual, private,
reserved, timid, self-sacrificing, gifted
Animation, Film-Noir,
Parody, Cult
SimProfile
• Determine psychological profile using a 36 yes/no psychological test;
• Determine % from the four main psychological types: choleric,
sanguine, phlegmatic, melancholic;
• Use this distribution to filter out the movies.
• Assumption: people with similar profiles watch similar genre of
movies.
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8. SimProfile (2)
• For example, for the “Top 100 Greatest Movies of All
Time”, no of movies which might be enjoyed by each
psychological profile are:
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Profile No. Movies
Choleric 57
Sanguine 84
Phlegmatic 65
Melancholic 8
• Compute, for a given movie,
the probability that a user
with a specific profile would
enjoy watching it (simProfile).

9. SimRatings
• The difference between the ratings of two
movies obtained from the IMDB platform:
simRatings = 1 - abs(rmi - rmj) / 10
• Assumption: if a user tends to watch good,
awarded movies, he/she will prefer that kind of
movies , instead of a movie rated by other users
as being bad, uninteresting, inadequate, etc.
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10. • A movie is represented by a vector, where each
element is attributed to a different user and the
possible values are 1, -1 or 0.
• The similarity between 2 movies is computed using
cosine similarity:
• Assumption: similar movies have similar vectors.
• SimMovies of a movie mi from database is computed
using the similarity to the movies rated by the user:
SimMovies
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11. SimKNN
• Determine the users' similarity using the
Nearest Neighbor algorithm.
• Determine the closest users to the current one,
extract the movies that were positively rated
by them and not seen by the user and
recommend them to the user.
• Assumption: users having similar psychological
profiles will have similar preferences.
• Final score:
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12. Use Cases
• 1. New user: the system recommends the movies with the
most positive reviews from the database;
• 2. User has the profile completed, but haven’t rated any
movies yet: the system will be using two sources of
information: the associations between the movie genre and
the psychological profile (simProfile) and the movies positively
rated by the nearest users to the current user (simKNN);
• 3. User that has a complete psychological profile and has
given ratings to movies on the website, all four features are
used in recommendation;
• 4. User that doesn’t have the profile set up (hadn't
answered the questionnaire), but has given ratings: avoided
 user is first asked to fill in the questionnaire.
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13. Conclusions
• Presented a movie recommender system that
uses the users' psychological profile and
information extracted from the IMDb.
• Current implementation suffers from the
“cold start” problem  didn’t have enough
time to gather the sufficient information for
reliably predicting the user preferences.
• Since the system is based on the psychological
profile evaluation, it is difficult to evaluate its
accuracy (there are available datasets with
movie ratings, but they lack the psychological
profile of the users  unusable).
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14. Future Work
• Including in the recommendation algorithms of
additional sources of information, relevant for
the users preferences, such as:
– the users' gender, as suggested by Redfern or
– the users' age, (assuming that people from different
time periods might have a tendency to prefer movies
from their time).
• Additional movie features (casting, directors,
scenarists, writers, etc.) could also improve the
recommendation.
• Perform psychological studies to better
determine what movie genres can be associated
to a user psychological profile.
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15. Questions
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Thank you very much!
This work has been partially funded by the Sectoral Operational Programme
Human Resources Development 2007- 2013 of the Ministry of European Funds
through the Financial Agreement POSDRU/159/1.5/S/132395 and by the FP7
project LTfLL (Language Technologies for Lifelong Learning).