用大模型嵌入和共现模式提升个性化新闻推荐效果
Personalized News Recommendation System via LLM Embedding and Co-Occurrence Patterns
- 用对比学习微调大模型生成新闻语义嵌入
- 挖掘三种由大模型生成的共现模式，捕捉协同信息
- 首次通过大模型构建细粒度共现模式，适合推荐系统研究者
近两年，大语言模型（LLMs）快速发展并展现出强大的新兴能力。凭借强大的语义理解与推理能力，LLMs显著推动了推荐系统领域的进步。在新闻推荐（NR）中，系统需处理大量点击新闻文本以预测候选新闻的点击概率，这一需求超出传统模型能力，却正契合LLMs优势。本文提出一种新型NR算法LECOP，通过LLM嵌入与共现模式重构新闻模型。一方面，利用大规模数据集对LLM进行对比学习微调，编码新闻内容，充分挖掘语义信息以精准识别用户偏好；另一方面，探索多种共现模式，包括新闻ID共现、物品-物品关键词共现和物品内关键词共现，所有关键词均由LLM生成。据我们所知，这是首次通过LLM构建如此细致的共现模式以捕捉协同信号。大量实验表明，该方法性能优越。
原文摘要 · Abstract (English)
In the past two years, large language models (LLMs) have achieved rapid development and demonstrated remarkable emerging capabilities. Concurrently, with powerful semantic understanding and reasoning capabilities, LLMs have significantly empowered the rapid advancement of the recommendation system field. Specifically, in news recommendation (NR), systems must comprehend and process a vast amount of clicked news text to infer the probability of candidate news clicks. This requirement exceeds the capabilities of traditional NR models but aligns well with the strengths of LLMs. In this paper, we propose a novel NR algorithm to reshape the news model via LLM Embedding and Co-Occurrence Pattern (LECOP). On one hand, we fintuned LLM by contrastive learning using large-scale datasets to encode news, which can fully explore the semantic information of news to thoroughly identify user preferences. On the other hand, we explored multiple co-occurrence patterns to mine collaborative information. Those patterns include news ID co-occurrence, Item-Item keywords co-occurrence and Intra-Item keywords co-occurrence. The keywords mentioned above are all generated by LLM. As far as we know, this is the first time that constructing such detailed Co-Occurrence Patterns via LLM to capture collaboration. Extensive experiments demonstrate the superior performance of our proposed novel method
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