轻量级模型融合文本与类别特征，提升跨域推荐精度
LightFusionRec: Lightweight Transformers-Based Cross-Domain Recommendation Model
- 用DistilBERT和FastText提取文本与类型嵌入
- 在电影和书籍数据集上显著优于传统方法
- 适合资源受限设备上的实时推荐
本文提出LightFusionRec，一种基于轻量级Transformer的跨域推荐模型，结合DistilBERT进行文本特征提取，FastText生成类型嵌入。该模型有效缓解推荐系统中的数据稀疏、计算效率低和冷启动问题。通过融合类型向量与自然语言处理算法，仅用少量信息即可为多种媒体形式生成精准且上下文相关的推荐。在大规模电影与书籍数据集上的实验表明，其推荐质量显著优于传统方法。由于模型轻量化设计，支持多种应用场景及本地设备推理。LightFusionRec为数字内容平台提供高效可扩展的跨域推荐方案。
原文摘要 · Abstract (English)
This paper presents LightFusionRec, a novel lightweight cross-domain recommendation system that integrates DistilBERT for textual feature extraction and FastText for genre embedding. Important issues in recommendation systems, such as data sparsity, computational efficiency, and cold start issues, are addressed in methodology. LightFusionRec uses a small amount of information to produce precise and contextually relevant recommendations for many media formats by fusing genre vector embedding with natural language processing algorithms. Tests conducted on extensive movie and book datasets show notable enhancements in suggestion quality when compared to conventional methods. Because of its lightweight design, the model can be used for a variety of purposes and allows for ondevice inference. LightFusionRec is a noteworthy development in cross-domain recommendation systems, providing accurate and scalable recommendations to improve user experience on digital content platforms.
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