LANCER: A Lifetime-Aware News Recommender System


  • Hong-Kyun Bae Hanyang University
  • Jeewon Ahn Hanyang University
  • Dongwon Lee The Pennsylvania State University
  • Sang-Wook Kim Hanyang University



DMKM: Recommender Systems, DMKM: Web Search & Information Retrieval, APP: Web


From the observation that users reading news tend to not click outdated news, we propose the notion of 'lifetime' of news, with two hypotheses: (i) news has a shorter lifetime, compared to other types of items such as movies or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.




How to Cite

Bae, H.-K., Ahn, J., Lee, D., & Kim, S.-W. (2023). LANCER: A Lifetime-Aware News Recommender System. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 4141-4148.



AAAI Technical Track on Data Mining and Knowledge Management