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20232026
most citedPre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models

1 citations · 5 across the 15 of their papers we have counts for

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11 papers · 1 filter

cs.IR2026

OneReason Technical Report

OneRec Team, Biao Yang, Boyang Ding +81

Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce. Howev…

cs.IR20251 cited

Empowering Denoising Sequential Recommendation with Large Language Model Embeddings

Tongzhou Wu, Yuhao Wang, Maolin Wang +2

Sequential recommendation aims to capture user preferences by modeling sequential patterns in user-item interactions. However, these models are often influenced by noise such as ac…

cs.IR2025

Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs

Yuhao Wang, Junwei Pan, Xinhang Li +6

Sequential recommendation (SR) aims to capture users' dynamic interests and sequential patterns based on their historical interactions. Recently, the powerful capabilities of large…

cs.IR2025

Joint Modeling in Recommendations: A Survey

Xiangyu Zhao, Yichao Wang, Bo Chen +7

In today's digital landscape, Deep Recommender Systems (DRS) play a crucial role in navigating and customizing online content for individual preferences. However, conventional meth…

cs.IR2024

Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark

Xiaopeng Li, Jingtong Gao, Pengyue Jia +7

Multi Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained much attention. Ho…

cs.IR2024

Large Language Model Enhanced Recommender Systems: A Survey

Qidong Liu, Xiangyu Zhao, Yuhao Wang +9

Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the…