Pretrained Language Models (PLM) have been greatly successful on a board
range of natural language processing (NLP) tasks. However, it has just started
being applied to the domain of recommendation systems. Traditional
recommendation algorithms failed to incorporate the rich textual information in
e-commerce datasets, which hinderss the performance of those models. We present
a thorough investigation on the effect of various strategy of incorporating
PLMs into traditional recommender algorithms on one of the e-commerce datasets,
and we compare the results with vanilla recommender baseline models. We show
that the application of PLMs and domain specific fine-tuning lead to an
increase on the predictive capability of combined models. These results
accentuate the importance of utilizing textual information in the context of
e-commerce, and provides insight on how to better apply PLMs alongside
traditional recommender system algorithms. The code used in this paper is
available on Github: this https URL.

本文研究了将预训练语言模型应用于传统推荐算法中对电子商务数据集的影响，并将结果与基准模型进行了比较。通过领域特定的微调，将 PLMs 应用于传统推荐算法中可以提高模型的预测能力。这些结果凸显了在电子商务环境中利用文本信息的重要性，并提供了更好地应用 PLMs 的见解。