Enhancing High-order Interaction Awareness in LLM-based Recommender Model
- URL: http://arxiv.org/abs/2409.19979v3
- Date: Mon, 18 Nov 2024 06:28:01 GMT
- Title: Enhancing High-order Interaction Awareness in LLM-based Recommender Model
- Authors: Xinfeng Wang, Jin Cui, Fumiyo Fukumoto, Yoshimi Suzuki,
- Abstract summary: This paper presents an enhanced LLM-based recommender (ELMRec)
We enhance whole-word embeddings to substantially enhance LLMs' interpretation of graph-constructed interactions for recommendations.
Our ELMRec outperforms state-of-the-art (SOTA) methods in both direct and sequential recommendations.
- Score: 3.7623606729515133
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) have demonstrated prominent reasoning capabilities in recommendation tasks by transforming them into text-generation tasks. However, existing approaches either disregard or ineffectively model the user-item high-order interactions. To this end, this paper presents an enhanced LLM-based recommender (ELMRec). We enhance whole-word embeddings to substantially enhance LLMs' interpretation of graph-constructed interactions for recommendations, without requiring graph pre-training. This finding may inspire endeavors to incorporate rich knowledge graphs into LLM-based recommenders via whole-word embedding. We also found that LLMs often recommend items based on users' earlier interactions rather than recent ones, and present a reranking solution. Our ELMRec outperforms state-of-the-art (SOTA) methods in both direct and sequential recommendations.
Related papers
- KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation [8.64897967325355]
Large Language Models (LLMs) have shown strong potential in recommender systems due to their contextual learning and generalisation capabilities.<n>We propose a novel model called Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation (KERAG_R)<n>Specifically, we leverage a graph retrieval-augmented generation (GraphRAG) component to integrate additional information from a knowledge graph into instructions.<n>Our experiments on three public datasets show that our proposed KERAG_R model significantly outperforms ten existing state-of-the-art recommendation methods.
arXiv Detail & Related papers (2025-07-08T10:44:27Z) - LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation [49.78419076215196]
Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items.<n>Traditional sequential recommenders rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns.<n>Recent advances in large language models (LLMs) have motivated text-based recommendation approaches that derive item representations from textual descriptions.<n>We argue that an ideal embedding model should seamlessly integrate CF signals with rich semantic representations to improve both in-domain and out-of-domain recommendation performance.
arXiv Detail & Related papers (2025-06-16T13:27:06Z) - What LLMs Miss in Recommendations: Bridging the Gap with Retrieval-Augmented Collaborative Signals [4.297070083645049]
User-item interactions contain rich collaborative signals that form the backbone of many successful recommender systems.<n>It remains unclear whether large language models (LLMs) can effectively reason over this type of collaborative information.<n>We introduce a simple retrieval-augmented generation (RAG) method that enhances LLMs by grounding their predictions in structured interaction data.
arXiv Detail & Related papers (2025-05-27T05:18:57Z) - DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation [83.21140655248624]
Large language models (LLMs) have been introduced into recommender systems (RSs)<n>We propose DeepRec, a novel LLM-based RS that enables autonomous multi-turn interactions between LLMs and TRMs for deep exploration of the item space.<n> Experiments on public datasets demonstrate that DeepRec significantly outperforms both traditional and LLM-based baselines.
arXiv Detail & Related papers (2025-05-22T15:49:38Z) - RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning [22.495874056980824]
We propose Representation learning and textbfReasoning empowered retrieval-textbfAugmented textbfLarge textbfLanguage model textbfRecommendation (RALLRec+).
arXiv Detail & Related papers (2025-03-26T11:03:34Z) - Lost in Sequence: Do Large Language Models Understand Sequential Recommendation? [33.92662524009036]
Large Language Models (LLMs) have emerged as promising tools for recommendation thanks to their advanced textual understanding ability and context-awareness.
We propose a method that enhances the integration of sequential information into LLMs by distilling the user representations extracted from a pre-trained-SRec model into LLMs.
Our experiments show that LLM-SRec enhances LLMs' ability to understand users' item interaction sequences, ultimately leading to improved recommendation performance.
arXiv Detail & Related papers (2025-02-19T17:41:09Z) - Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation [44.685176786857284]
We propose ReLLaX (Retrieval-enhanced Large Language models Plus), a framework offering optimization across data, prompt, and parameter levels.
At the data level, we introduce Semantic User Behavior Retrieval (SUBR) to reduce sequence heterogeneity, making it easier for LLMs to extract key information.
For prompt-level enhancement, we employ Soft Prompt Augmentation (SPA) to inject collaborative knowledge, aligning item representations with recommendation tasks.
At the parameter level, we propose Component Fully-interactive LoRA (CFLoRA), which enhances LoRA's expressiveness by enabling interactions between its components
arXiv Detail & Related papers (2025-01-23T03:05:13Z) - RLRF4Rec: Reinforcement Learning from Recsys Feedback for Enhanced Recommendation Reranking [33.54698201942643]
Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains.
This paper introduces RLRF4Rec, a novel framework integrating Reinforcement Learning from Recsys Feedback for Enhanced Recommendation Reranking.
arXiv Detail & Related papers (2024-10-08T11:42:37Z) - Large Language Model Empowered Embedding Generator for Sequential Recommendation [57.49045064294086]
Large Language Model (LLM) has the potential to understand the semantic connections between items, regardless of their popularity.
We present LLMEmb, an innovative technique that harnesses LLM to create item embeddings that bolster the performance of Sequential Recommender Systems.
arXiv Detail & Related papers (2024-09-30T03:59:06Z) - DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System [83.34921966305804]
Large language models (LLMs) have demonstrated remarkable performance in recommender systems.
We propose a novel plug-and-play alignment framework for LLMs and collaborative models.
Our method is superior to existing state-of-the-art algorithms.
arXiv Detail & Related papers (2024-08-15T15:56:23Z) - Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation [83.87767101732351]
Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences.
Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS.
We propose DARec, a sequential recommendation model built on top of coarse-grained adaption for capturing inter-item relations.
arXiv Detail & Related papers (2024-08-14T10:03:40Z) - One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs)
We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
arXiv Detail & Related papers (2024-05-30T03:44:54Z) - Improve Temporal Awareness of LLMs for Sequential Recommendation [61.723928508200196]
Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks.
LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data.
We propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation.
arXiv Detail & Related papers (2024-05-05T00:21:26Z) - Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation [23.182787000804407]
Large Language Models (LLMs) are emerging as promising approaches to enhance session-based recommendation (SBR)
We propose a Reflective Reinforcement Large Language Model (Re2LLM) for SBR, guiding LLMs to focus on specialized knowledge essential for more accurate recommendations.
arXiv Detail & Related papers (2024-03-25T05:12:18Z) - LlamaRec: Two-Stage Recommendation using Large Language Models for
Ranking [10.671747198171136]
We propose a two-stage framework using large language models for ranking-based recommendation (LlamaRec)
In particular, we use small-scale sequential recommenders to retrieve candidates based on the user interaction history.
LlamaRec consistently achieves datasets superior performance in both recommendation performance and efficiency.
arXiv Detail & Related papers (2023-10-25T06:23:48Z) - LLMRec: Benchmarking Large Language Models on Recommendation Task [54.48899723591296]
The application of Large Language Models (LLMs) in the recommendation domain has not been thoroughly investigated.
We benchmark several popular off-the-shelf LLMs on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization.
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
arXiv Detail & Related papers (2023-08-23T16:32:54Z) - 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)
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.