Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction

The

st
1

UTS-VNU Research School

Advanced Technologies for IoT Applications
Title: Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction
Author Names and Affiliations: Xanh Ho and Nhung T.H. Nguyen – VNUHCM University of Science
Abstract: Approaches for film recommendation systems usually exploit explicit descriptive features to compute
ratings. In this paper, we suggest a different approach – to rate films via their related neighbors computed via
distributed representation of movies. Specifically, we present Film2Vec, a distributed representation learning
for films adapted from the distributed hypothesis from linguistics. We implement our proposed idea using
TensorFlow, a Google’s Deep Neural Networks software. The experimental results on Movielens dataset show
that Film2Vec can effectively reduce root mean square error (RMSE) in movie recommendation task, suggesting
yet another beneficial application of deep learning.

Problem Statement
Recommendation systems

Recommend

Many works use
rating information

Contributions
Film2Vec – Representing Films as Vectors

Few works use context of
recommendation system

Context of film:
• Title
• Actors - A
• Tags - T
• Genres - G
• Directors - D

Results
1.1

Pre-processing

HetRec 2011
Film1
Film2
Film3
…
...
Filmn

A1 D120 G19 T18
A13 D14 G156 T17
A12 D23 G43 T65

A45

D2

G4

Film descriptions

T1

Root Mean Square Error

1.05
1
0.95
0.9
0.85
0.8
0.75
0.7

F2V-TA F2V-TDGA

Film vectors

Film2Vec

Worst F2V-TA

CF

CA

Best F2V-TDGA

ARR

LLS

IMBRF

Previous approaches

Future work

References

• Use other information of film such as
countries, location and plot.
• Apply to other areas such as books,
services, and papers.

[1] Baroni et al. “Don’t count, predict! A systematic comparison of context-counting vs
context-predicting semantic vectors”, ACL, 2014.
[2] Bothos et al. “Information market based recommender systems fusion”, HetRec, 2011.
[3] Mikolov et al. “Efficient Estimation of Word Representations in Vector Space”, ICLR,
2013.