TY - JOUR
T1 - Exploiting Aesthetic Features in Visual Contents for Movie Recommendation
AU - Chen, Xiaojie
AU - Zhao, Pengpeng
AU - Liu, Yanchi
AU - Zhao, Lei
AU - Fang, Junhua
AU - Sheng, Victor S.
AU - Cui, Zhiming
N1 - Publisher Copyright:
© 2013 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - As one of the most widely used recommender systems, movie recommendation plays an important role in our life. However, the data sparsity problem severely hinders the effectiveness of personalized movie recommendation, which requires more rich content information to be utilized. Posters and still frames, which directly display the visual contents of movies, have significant influences on movie recommendation. They not only reveal rich knowledge for understanding movies but also useful for understanding user preferences. However, existing recommendation methods rarely consider aesthetic features, which tell how the movie looks and feels, extracted from these pictures for the movie recommendation. To this end, in this paper, we propose an aesthetic-aware unified visual content matrix factorization (called UVMF-AES) to integrate visual feature learning and recommendation into a unified framework. Specifically, we first integrate the convolutional neural network (CNN) features and aesthetic features into probabilistic matrix factorization. Then we establish a unified optimization framework with these features for the movie recommendation. The experimental results on two real-world datasets show that our proposed method UVMF-AES is significantly superior to the state-of-the-art methods on movie recommendation.
AB - As one of the most widely used recommender systems, movie recommendation plays an important role in our life. However, the data sparsity problem severely hinders the effectiveness of personalized movie recommendation, which requires more rich content information to be utilized. Posters and still frames, which directly display the visual contents of movies, have significant influences on movie recommendation. They not only reveal rich knowledge for understanding movies but also useful for understanding user preferences. However, existing recommendation methods rarely consider aesthetic features, which tell how the movie looks and feels, extracted from these pictures for the movie recommendation. To this end, in this paper, we propose an aesthetic-aware unified visual content matrix factorization (called UVMF-AES) to integrate visual feature learning and recommendation into a unified framework. Specifically, we first integrate the convolutional neural network (CNN) features and aesthetic features into probabilistic matrix factorization. Then we establish a unified optimization framework with these features for the movie recommendation. The experimental results on two real-world datasets show that our proposed method UVMF-AES is significantly superior to the state-of-the-art methods on movie recommendation.
KW - Movie recommendation
KW - aesthetic features
KW - probabilistic matrix factorization
UR - http://www.scopus.com/inward/record.url?scp=85065125107&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2910722
DO - 10.1109/ACCESS.2019.2910722
M3 - Article
AN - SCOPUS:85065125107
VL - 7
SP - 49813
EP - 49821
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 8688388
ER -