Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

• URL: http://arxiv.org/abs/2305.07001v1
• Date: Thu, 11 May 2023 17:39:07 GMT
• Title: Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
• Authors: Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, Ji-Rong Wen
• Abstract summary: We consider recommendation as instruction following by large language models (LLMs) We first design a general instruction format for describing the preference, intention, task form and context of a user in natural language. Then we manually design 39 instruction templates and automatically generate a large amount of user-personalized instruction data.
• Score: 83.62750225073341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effective recommendation models. Basically speaking, these models mainly learn the underlying user preference from historical behavior data, and then estimate the user-item matching relationships for recommendations. Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs. The key idea is that the preferences or needs of a user can be expressed in natural language descriptions (called instructions), so that LLMs can understand and further execute the instruction for fulfilling the recommendation task. Instead of using public APIs of LLMs, we instruction tune an open-source LLM (3B Flan-T5-XL), in order to better adapt LLMs to recommender systems. For this purpose, we first design a general instruction format for describing the preference, intention, task form and context of a user in natural language. Then we manually design 39 instruction templates and automatically generate a large amount of user-personalized instruction data (252K instructions) with varying types of preferences and intentions. To demonstrate the effectiveness of our approach, we instantiate the instruction templates into several widely-studied recommendation (or search) tasks, and conduct extensive experiments on these tasks with real-world datasets. Experiment results show that the proposed approach can outperform several competitive baselines, including the powerful GPT-3.5, on these evaluation tasks. Our approach sheds light on developing more user-friendly recommender systems, in which users can freely communicate with the system and obtain more accurate recommendations via natural language instructions.

Related papers

• A Prompting-Based Representation Learning Method for Recommendation with Large Language Models [2.1161973970603998]
  We introduce the Prompting-Based Representation Learning Method for Recommendation (P4R) to boost the linguistic abilities of Large Language Models (LLMs) in Recommender Systems. In our P4R framework, we utilize the LLM prompting strategy to create personalized item profiles. In our evaluation, we compare P4R with state-of-the-art Recommender models and assess the quality of prompt-based profile generation.
  arXiv  Detail & Related papers  (2024-09-25T07:06:14Z)
• Laser: Parameter-Efficient LLM Bi-Tuning for Sequential Recommendation with Collaborative Information [76.62949982303532]
  We propose a parameter-efficient Large Language Model Bi-Tuning framework for sequential recommendation with collaborative information (Laser) In our Laser, the prefix is utilized to incorporate user-item collaborative information and adapt the LLM to the recommendation task, while the suffix converts the output embeddings of the LLM from the language space to the recommendation space for the follow-up item recommendation. M-Former is a lightweight MoE-based querying transformer that uses a set of query experts to integrate diverse user-specific collaborative information encoded by frozen ID-based sequential recommender systems.
  arXiv  Detail & Related papers  (2024-09-03T04:55:03Z)
• Preference Distillation for Personalized Generative Recommendation [11.27949757550442]
  We propose a PErsonAlized PrOmpt Distillation (PeaPOD) approach to distill user preferences as personalized soft prompts. Considering the complexities of user preferences in the real world, we maintain a shared set of learnable prompts that are dynamically weighted based on the user's interests. Experimental results on three real-world datasets demonstrate the effectiveness of our PeaPOD model on sequential recommendation, top-n recommendation, and explanation generation tasks.
  arXiv  Detail & Related papers  (2024-07-06T09:58:58Z)
• Unlocking the Potential of Large Language Models for Explainable Recommendations [55.29843710657637]
  It remains uncertain what impact replacing the explanation generator with the recently emerging large language models (LLMs) would have. In this study, we propose LLMXRec, a simple yet effective two-stage explainable recommendation framework. By adopting several key fine-tuning techniques, controllable and fluent explanations can be well generated.
  arXiv  Detail & Related papers  (2023-12-25T09:09:54Z)
• RecExplainer: Aligning Large Language Models for Explaining Recommendation Models [50.74181089742969]
  Large language models (LLMs) have demonstrated remarkable intelligence in understanding, reasoning, and instruction following. This paper presents the initial exploration of using LLMs as surrogate models to explain black-box recommender models. To facilitate an effective alignment, we introduce three methods: behavior alignment, intention alignment, and hybrid alignment.
  arXiv  Detail & Related papers  (2023-11-18T03:05:43Z)
• Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling [18.297332953450514]
  We propose LANCER, which leverages the semantic understanding capabilities of pre-trained language models to generate personalized recommendations. Our approach bridges the gap between language models and recommender systems, resulting in more human-like recommendations.
  arXiv  Detail & Related papers  (2023-09-19T08:54:47Z)
• LLM-Rec: Personalized Recommendation via Prompting Large Language Models [62.481065357472964]
  Large language models (LLMs) have showcased their ability to harness commonsense knowledge and reasoning. Recent advances in large language models (LLMs) have showcased their remarkable ability to harness commonsense knowledge and reasoning. This study introduces a novel approach, coined LLM-Rec, which incorporates four distinct prompting strategies of text enrichment for improving personalized text-based recommendations.
  arXiv  Detail & Related papers  (2023-07-24T18:47:38Z)
• GenRec: Large Language Model for Generative Recommendation [41.22833600362077]
  This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. GenRec uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our research underscores the potential of LLM-based generative recommendation in revolutionizing the domain of recommendation systems.
  arXiv  Detail & Related papers  (2023-07-02T02:37:07Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.