# Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model

arXiv: https://arxiv.org/abs/2310.16409 · HTML: https://mlsanity.com/paper/2310.16409/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [Is ChatGPT a Good Recommender? A Preliminary Study](https://mlsanity.com/paper/2304.10149/) (2023)
- [Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach](https://mlsanity.com/paper/2305.07001/) (2023)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [Translation-based Recommendation](https://mlsanity.com/paper/1707.02410/) (2017)
- [Decoupled Side Information Fusion for Sequential Recommendation](https://mlsanity.com/paper/2204.11046/) (2022)
- [The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study](https://mlsanity.com/paper/1912.04696/) (2019)
- [Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems](https://mlsanity.com/paper/2302.14532/) (2023)

## Built on by (most cited first, 1 of 1)

- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)

## Abstract (arXiv)

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.