TY - GEN
T1 - LANCER
T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023
AU - Bae, Hong Kyun
AU - Ahn, Jeewon
AU - Lee, Dongwon
AU - Kim, Sang Wook
N1 - Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2023/6/27
Y1 - 2023/6/27
N2 - From the observation that users reading news tend to not click outdated news, we propose the notion of ‘lifetime’ of news, with two hypotheses: (i) news has a shorter lifetime, compared to other types of items such as movies or e-commerce products; (ii) news only competes with other news whose lifetimes have not ended, and which has an overlapping lifetime (i.e., limited competitions). By further developing the characteristics of the lifetime of news, then we present a novel approach for news recommendation, namely, Lifetime-Aware News reCommEndeR System (LANCER) that carefully exploits the lifetime of news during training and recommendation. Using real-world news datasets (e.g., Adressa and MIND), we successfully demonstrate that state-of-the-art news recommendation models can get significantly benefited by integrating the notion of lifetime and LANCER, by up to about 40% increases in recommendation accuracy.
AB - From the observation that users reading news tend to not click outdated news, we propose the notion of ‘lifetime’ of news, with two hypotheses: (i) news has a shorter lifetime, compared to other types of items such as movies or e-commerce products; (ii) news only competes with other news whose lifetimes have not ended, and which has an overlapping lifetime (i.e., limited competitions). By further developing the characteristics of the lifetime of news, then we present a novel approach for news recommendation, namely, Lifetime-Aware News reCommEndeR System (LANCER) that carefully exploits the lifetime of news during training and recommendation. Using real-world news datasets (e.g., Adressa and MIND), we successfully demonstrate that state-of-the-art news recommendation models can get significantly benefited by integrating the notion of lifetime and LANCER, by up to about 40% increases in recommendation accuracy.
UR - https://www.scopus.com/pages/publications/85167870491
UR - https://www.scopus.com/pages/publications/85167870491#tab=citedBy
U2 - 10.1609/aaai.v37i4.25530
DO - 10.1609/aaai.v37i4.25530
M3 - Conference contribution
AN - SCOPUS:85167870491
T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
SP - 4141
EP - 4148
BT - AAAI-23 Technical Tracks 4
A2 - Williams, Brian
A2 - Chen, Yiling
A2 - Neville, Jennifer
PB - AAAI press
Y2 - 7 February 2023 through 14 February 2023
ER -