Related papers: End-to-End Personalization: Unifying Recommender Systems with Large Language Models

End-to-End Personalization: Unifying Recommender Systems with Large Language Models

URL: http://arxiv.org/abs/2508.01514v1
Date: Sat, 02 Aug 2025 22:46:50 GMT
Title: End-to-End Personalization: Unifying Recommender Systems with Large Language Models
Authors: Danial Ebrat, Tina Aminian, Sepideh Ahmadian, Luis Rueda,
Abstract summary: We propose a novel hybrid recommendation framework that combines Graph Attention Networks (GATs) with Large Language Models (LLMs)<n>LLMs are first used to enrich user and item representations by generating semantically meaningful profiles based on metadata such as titles, genres, and overviews.<n>We evaluate our model on benchmark datasets, including MovieLens 100k and 1M, where it consistently outperforms strong baselines.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Recommender systems are essential for guiding users through the vast and diverse landscape of digital content by delivering personalized and relevant suggestions. However, improving both personalization and interpretability remains a challenge, particularly in scenarios involving limited user feedback or heterogeneous item attributes. In this article, we propose a novel hybrid recommendation framework that combines Graph Attention Networks (GATs) with Large Language Models (LLMs) to address these limitations. LLMs are first used to enrich user and item representations by generating semantically meaningful profiles based on metadata such as titles, genres, and overviews. These enriched embeddings serve as initial node features in a user and movie bipartite graph, which is processed using a GAT based collaborative filtering model. To enhance ranking accuracy, we introduce a hybrid loss function that combines Bayesian Personalized Ranking (BPR), cosine similarity, and robust negative sampling. Post-processing involves reranking the GAT-generated recommendations using the LLM, which also generates natural-language justifications to improve transparency. We evaluated our model on benchmark datasets, including MovieLens 100k and 1M, where it consistently outperforms strong baselines. Ablation studies confirm that LLM-based embeddings and the cosine similarity term significantly contribute to performance gains. This work demonstrates the potential of integrating LLMs to improve both the accuracy and interpretability of recommender systems.

Related papers

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)
LLM-Augmented Graph Neural Recommenders: Integrating User Reviews [2.087411180679868]
We propose a framework that employs a Graph Neural Network (GNN)-based model and an large language model (LLMs) to produce review-aware representations.<n>Our approach balances user-item interactions against text-derived features, ensuring that user's both behavioral and linguistic signals are effectively captured.
arXiv Detail & Related papers (2025-04-03T00:40:09Z)
Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment.<n>By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs.<n> Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
arXiv Detail & Related papers (2025-02-26T02:19:10Z)
RecLM: Recommendation Instruction Tuning [17.780484832381994]
We propose a model-agnostic recommendation instruction-tuning paradigm that seamlessly integrates large language models with collaborative filtering.<n>Our proposed $underlineRec$ommendation enhances the capture of user preference diversity through a carefully designed reinforcement learning reward function.
arXiv Detail & Related papers (2024-12-26T17:51:54Z)
ULMRec: User-centric Large Language Model for Sequential Recommendation [16.494996929730927]
We propose ULMRec, a framework that integrates user personalized preferences into Large Language Models.<n>Extensive experiments on two public datasets demonstrate that ULMRec significantly outperforms existing methods.
arXiv Detail & Related papers (2024-12-07T05:37:00Z)
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation [57.49045064294086]
Large Language Model (LLM) has the ability to capture semantic relationships between items, independent of their popularity.<n>We introduce LLMEmb, a novel method leveraging LLM to generate item embeddings that enhance Sequential Recommender Systems (SRS) performance.
arXiv Detail & Related papers (2024-09-30T03:59:06Z)
Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator [60.07198935747619]
We propose Twin-Tower Dynamic Semantic Recommender (T TDS), the first generative RS which adopts dynamic semantic index paradigm. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender. The proposed T TDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.
arXiv Detail & Related papers (2024-09-14T01:45:04Z)
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)
Aligning Large Language Models via Fine-grained Supervision [20.35000061196631]
Pre-trained large-scale language models (LLMs) excel at producing coherent articles, yet their outputs may be untruthful, toxic, or fail to align with user expectations. Current approaches focus on using reinforcement learning with human feedback to improve model alignment. We propose a method to enhance LLM alignment through fine-grained token-level supervision.
arXiv Detail & Related papers (2024-06-04T20:21:45Z)
Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment [104.18002641195442]
We introduce Self-Augmented Preference Optimization (SAPO), an effective and scalable training paradigm that does not require existing paired data. Building on the self-play concept, which autonomously generates negative responses, we further incorporate an off-policy learning pipeline to enhance data exploration and exploitation.
arXiv Detail & Related papers (2024-05-31T14:21:04Z)

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