TY - GEN
T1 - Sain
T2 - 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019
AU - Yun, Seoungjun
AU - Kim, Raehyun
AU - Ko, Miyoung
AU - Kang, Jaewoo
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF-2017R1A2A1A17069645, NRF-2017M3C4A7065887)
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/18
Y1 - 2019/7/18
N2 - With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%.
AB - With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%.
KW - Datasets
KW - Gaze detection
KW - Neural networks
KW - Text tagging
UR - http://www.scopus.com/inward/record.url?scp=85073802746&partnerID=8YFLogxK
U2 - 10.1145/3331184.3331342
DO - 10.1145/3331184.3331342
M3 - Conference contribution
AN - SCOPUS:85073802746
T3 - SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 1205
EP - 1208
BT - SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery, Inc
Y2 - 21 July 2019 through 25 July 2019
ER -