TY - GEN
T1 - Attention Mechanism indicating Item Novelty for Sequential Recommendation
AU - Wang, Li Chia
AU - Ma, Hao Shang
AU - Huang, Jen Wei
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Most sequential recommendation systems, including those that employ a variety of features and state-of-the-art network models, tend to favor items that are the most popular or of greatest relevance to the historic behavior of the user. Recommendations made under these conditions tend to be repetitive; i.e., many options that might be of interest to users are entirely disregarded. This paper presents a novel algorithm that assigns a novelty score to potential recommendation items. We also present an architecture by which to incorporate this functionality in existing recommendation systems. In experiments, the proposed NASM system outperformed state-of-the-art sequential recommender systems, thereby verifying that the inclusion of novelty score can indeed improve recommendation performance.
AB - Most sequential recommendation systems, including those that employ a variety of features and state-of-the-art network models, tend to favor items that are the most popular or of greatest relevance to the historic behavior of the user. Recommendations made under these conditions tend to be repetitive; i.e., many options that might be of interest to users are entirely disregarded. This paper presents a novel algorithm that assigns a novelty score to potential recommendation items. We also present an architecture by which to incorporate this functionality in existing recommendation systems. In experiments, the proposed NASM system outperformed state-of-the-art sequential recommender systems, thereby verifying that the inclusion of novelty score can indeed improve recommendation performance.
UR - http://www.scopus.com/inward/record.url?scp=85151931389&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85151931389&partnerID=8YFLogxK
U2 - 10.1109/ASONAM55673.2022.10068599
DO - 10.1109/ASONAM55673.2022.10068599
M3 - Conference contribution
AN - SCOPUS:85151931389
T3 - Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
SP - 176
EP - 180
BT - Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
A2 - An, Jisun
A2 - Charalampos, Chelmis
A2 - Magdy, Walid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
Y2 - 10 November 2022 through 13 November 2022
ER -