Sonia Bergamaschi, Laura Po and Serena Sorrentino

Department of Engineering “Enzo Ferrari”, University of

Modena and Reggio Emilia, Italy

Comparing Topic Models for a Movie Recommendation

System

Recommendation systems

their performance greatly

suffers when little information about the

users preferences are given

movie plots

without knowing any user

preferences

Topic Models

Local database

movie selected

by the user

NO personal

information

NO user

preferences

Internet Movie Database Open Movie Database

Cast&Crew

Movie Person

IMDB Movie

Collection 1,861,736

IMDB Personality

Collection 3,165,235

TMDB Film

Collection 20,861

IMDB Cast

Collection 24,662,392

TMDB Person

Collection 234,986

TMDB Production

Collection 225,494

English Dbpedia

Movie Collection 164,508

EnglishDbpedia

Crew Collection 6,102

German Dbpedia

Movie Collection 164,508

German Dbpedia

Crew Collection 866

English Dbpedia

Actor Collection 6,151

German Dbpedia

Actor Collection 1,039

1. Plot Vectorization -

2. Weights Computation-

3. Matrix Reduction by using Topic Models

4. Movie Similarity Computation-

keyword1 keyword2 …

plot a

plot b wb,2

plot c

The weight of keyword 2

according to plot b

lower

add movies

without re-computing

find similar movies

T Document by

Keyword Matrix (d x k)

K Topic by Keyword

Matrix (z x k)

= x S Topic by

Topic Matrix (z x z)

DT Document by Topic Matrix

(d x z)

x

P(k|d) Document

distribution over Keywords

(d x k)

P(k|z) Topic

distrib. over

Keywords (z x k)

= x

LSA

LDA

P(z|d) Document distrib.

over Topics (d x z)

204,000 plots x

220,000 keywords 204,000 plots x

500 topics

204,000 plots x

50 topics 204,000 plots x

220,000 keywords

A test on the IMDb database, about 1,8 million of

multimedia only 204,000 has a plot available.

LSA allows to select plots that are

better related to the target’s plot themes

Off-line tests

• 20 users

• 18 movies

• the top 6

recommendations

from both LSA and

LDA

• 594 evaluations

collected

LDA does not have good

performance on movie

recommendations: it is not able to

suggest movies of the same saga

and it suggests erroneous entries for

movies that have short plot

LSA achieves good performance

on movie recommendations:

it is able to suggest movies of the

same saga and also unknown

movies related to the target one

• 30 users

• 18 movies

• the top 6

recommendations

from both LDA and

IMDb

• 146 evaluations

collected