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Large pretrained language models (PLM) have become de facto news encoders inmodern news recommender systems, due to their strong ability in comprehendingtextual content. These huge Transformer-based architectures, when finetuned onrecommendation tasks, can greatly improve news recommendation performance.However, the PLM-based pretrain-finetune framework incurs high computationalcost and energy consumption, primarily due to the extensive redundantprocessing of news encoding during each training epoch. In this paper, wepropose the ``Only Encode Once'' framework for news recommendation (OLEO), bydecoupling news representation learning from downstream recommendation tasklearning. The decoupled design makes content-based news recommender as greenand efficient as id-based ones, leading to great reduction in computationalcost and training resources. Extensive experiments show that our OLEO frameworkcan reduce carbon emissions by up to 13 times compared with thestate-of-the-art pretrain-finetune framework and maintain a competitive or evensuperior performance level. The source code is released for reproducibility.

Only Encode Once: Making Content-based News Recommender Greener