Recommendation system (RS) plays significant roles in matching users
information needs for Internet applications, and it usually utilizes the
vanilla neural network as the backbone to handle embedding details. Recently,
the large language model (LLM) has exhibited emergent abilities and achieved
great breakthroughs both in the CV and NLP communities. Thus, it is logical to
incorporate RS with LLM better, which has become an emerging research
direction. Although some existing works have made their contributions to this
issue, they mainly consider the single key situation (e.g. historical
interactions), especially in sequential recommendation. The situation of
multiple key-value data is simply neglected. This significant scenario is
mainstream in real practical applications, where the information of users (e.g.
age, occupation, etc) and items (e.g. title, category, etc) has more than one
key. Therefore, we aim to implement sequential recommendations based on
multiple key-value data by incorporating RS with LLM. In particular, we
instruct tuning a prevalent open-source LLM (Llama 7B) in order to inject
domain knowledge of RS into the pre-trained LLM. Since we adopt multiple
key-value strategies, LLM is hard to learn well among these keys. Thus the
general and innovative shuffle and mask strategies, as an innovative manner of
data argument, are designed. To demonstrate the effectiveness of our approach,
extensive experiments are conducted on the popular and suitable dataset
MovieLens which contains multiple keys-value. The experimental results
demonstrate that our approach can nicely and effectively complete this
challenging issue.

基于多个键值数据，将推荐系统与大型语言模型相结合，通过培训一个常见的开源语言模型（Llama 7B），将推荐系统的领域知识注入预训练的语言模型，并设计了创新的洗牌和屏蔽策略来学习这些键值之间的关系，实验证明了这种方法的有效性。