改进推荐模型，提升冷启动新闻推荐准确率
Addressing Cold Start For next-article Recommendation
- 将音乐推荐模型改造成基于新闻点击序列的推荐方法
- 使用BERT和TF-IDF提取文本特征，在冷启动下表现更优
- 适合需要快速响应新用户/新内容的新闻推荐系统
本复现研究将用于下一首歌推荐的自适应线性映射模型（ALMM）改造为新闻推荐任务，基于MIND数据集进行实验。原版ALMM在张量分解框架中计算用户、上一篇和当前文章的潜在表示，并学习从内容特征到项目向量的线性映射。本文通过重构模型以适应连续新闻点击行为，将相继阅读的文章视为（前文，后文）三元组。采用BERT和TF-IDF对新闻标题与摘要提取上下文嵌入，并与基于三元组的用户阅读模式对齐。同时提出一个可复现的预处理流程，包含新闻过滤与特征完整性验证。结果表明，采用TF-IDF的ALMM实现版本在冷启动场景下相较Forbes和Oord基线模型表现出更高的推荐精度与鲁棒性。研究还证实，未经修改的ALMM在最小调整状态下不适用于下一则新闻推荐。
原文摘要 · Abstract (English)
This replication study modifies ALMM, the Adaptive Linear Mapping Model constructed for the next song recommendation, to the news recommendation problem on the MIND dataset. The original version of ALMM computes latent representations for users, last-time items, and current items in a tensor factorization structure and learns a linear mapping from content features to latent item vectors. Our replication aims to improve recommendation performance in cold-start scenarios by restructuring this model to sequential news click behavior, viewing consecutively read articles as (last news, next news) tuples. Instead of the original audio features, we apply BERT and a TF-IDF (Term Frequency-Inverse Document Frequency) to news titles and abstracts to extract token contextualized representations and align them with triplet-based user reading patterns. We also propose a reproducibly thorough pre-processing pipeline combining news filtering and feature integrity validation. Our implementation of ALMM with TF-IDF shows relatively improved recommendation accuracy and robustness over Forbes and Oord baseline models in the cold-start scenario. We demonstrate that ALMM in a minimally modified state is not suitable for next news recommendation.
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