@inproceedings{qi2022fum,
author = {Qi, Tao and Wu, Fangzhao and Wu, Chuhan and Huang, Yongfeng},
title = {FUM: Fine-grained and Fast User Modeling for News Recommendation},
booktitle = {SIGIR 2022},
year = {2022},
month = {April},
abstract = {User modeling is important for news recommendation. Existing methods usually first encode user's clicked news into news embeddings independently and then aggregate them into user embedding. However, the word-level interactions across different clicked news from the same user, which contain rich detailed clues to infer user interest, are ignored by these methods. In this paper, we propose a fine-grained and fast user modeling framework (FUM) to model user interest from fine-grained behavior interactions for news recommendation. The core idea of FUM is to concatenate the clicked news into a long document and transform user modeling into a document modeling task with both intra-news and inter-news word-level interactions. Since vanilla transformer cannot efficiently handle long document, we apply an efficient transformer named Fastformer to model fine-grained behavior interactions. Extensive experiments on two real-world datasets verify that FUM can effectively and efficiently model user interest for news recommendation.},
url = {http://approjects.co.za/?big=en-us/research/publication/fum-fine-grained-and-fast-user-modeling-for-news-recommendation/},
}