用注意力自编码器提升电影评分预测准确率
RSAttAE: An Information-Aware Attention-based Autoencoder Recommender System
- 设计基于注意力的自编码器提取用户-电影特征
- 在MovieLens 100K上达到优于主流方法的预测精度
- 适合对推荐系统精度有要求的研究者和开发者
推荐系统在信息检索、制药、零售和娱乐等领域至关重要，尤其娱乐行业关注度高、收益大。本文提出一种新方法，用于预测未知的用户-电影评分以提升客户满意度。基于MovieLens 100K数据集，采用注意力自编码器生成有意义的特征表示，并结合XGBoost进行评分预测。实验结果表明，该方法在多数现有先进方法中表现更优。
原文摘要 · Abstract (English)
Recommender systems play a crucial role in modern life, including information retrieval, the pharmaceutical industry, retail, and entertainment. The entertainment sector, in particular, attracts significant attention and generates substantial profits. This work proposes a new method for predicting unknown user-movie ratings to enhance customer satisfaction. To achieve this, we utilize the MovieLens 100K dataset. Our approach introduces an attention-based autoencoder to create meaningful representations and the XGBoost method for rating predictions. The results demonstrate that our proposal outperforms most of the existing state-of-the-art methods. Availability: github.com/ComputationIASBS/RecommSys
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