LANCER: A Lifetime-Aware News Recommender System
Abstract
From the observation that users reading news tend to not click outdated news, we propose the notion of 'lifetime' of news, with two hypotheses: (i) news has a shorter lifetime, compared to other types of items such as movies or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.
Cite
Text
Bae et al. "LANCER: A Lifetime-Aware News Recommender System." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I4.25530Markdown
[Bae et al. "LANCER: A Lifetime-Aware News Recommender System." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/bae2023aaai-lancer/) doi:10.1609/AAAI.V37I4.25530BibTeX
@inproceedings{bae2023aaai-lancer,
title = {{LANCER: A Lifetime-Aware News Recommender System}},
author = {Bae, Hong-Kyun and Ahn, Jeewon and Lee, Dongwon and Kim, Sang-Wook},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {4141-4148},
doi = {10.1609/AAAI.V37I4.25530},
url = {https://mlanthology.org/aaai/2023/bae2023aaai-lancer/}
}