融合BERT与CNN的推荐模型，提升用户兴趣捕捉能力
BERT and CNN integrated Neural Collaborative Filtering for Recommender Systems
- 结合BERT与CNN处理用户/物品的文本、类别和图像数据
- 在MovieLens上达0.72召回率与0.486命中率@10
- 适合需要多模态输入的推荐系统研究者参考
每天有大量用户访问互联网以满足不同需求，网站通过用户与内容或商品的互动获利。一个强大的推荐系统可通过根据用户独特偏好推荐项目来提升互动。本文提出一种融合BERT与卷积神经网络（CNN）的神经协同过滤（NCF）模型，该模型接收用户和物品的画像信息，挖掘用户兴趣。该模型能处理数值型、类别型及图像数据，从中提取潜在特征。在小样本MovieLens数据集上训练并验证25轮。相同数据集用于训练并对比简单NCF模型与基于BERT的NCF模型。实验结果表明，所提模型优于两个基线模型，在799名用户上取得0.72的召回率和0.486的命中率@10。研究结论为：同时考虑类别型与图像数据可提升推荐系统性能。
原文摘要 · Abstract (English)
Every day, a significant number of users visit the internet for different needs. The owners of a website generate profits from the user interaction with the contents or items of the website. A robust recommendation system can increase user interaction with a website by recommending items according to the user's unique preferences. BERT and CNN-integrated neural collaborative filtering (NCF) have been proposed for the recommendation system in this experiment. The proposed model takes inputs from the user and item profile and finds the user's interest. This model can handle numeric, categorical, and image data to extract the latent features from the inputs. The model is trained and validated on a small sample of the MovieLens dataset for 25 epochs. The same dataset has been used to train and validate a simple NCF and a BERT-based NCF model and compared with the proposed model. The proposed model outperformed those two baseline models. The obtained result for the proposed model is 0.72 recall and 0.486 Hit Ratio @ 10 for 799 users on the MovieLens dataset. This experiment concludes that considering both categorical and image data can improve the performance of a recommendation system.
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