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With the rapid development of online services, recommender systems (RS) have become increasingly indispensable for mitigating information overload.
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu. 2023 · 2023
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A Unified Multi-Task Learning Framework for Multi-Goal Conversational Recommender Systems
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Leveraging Large Language Models in Conversational Recommender Systems
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LLaMA-E: Empowering E-commerce Authoring with Multi-Aspect Instruction Following
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RAH! RecSys-Assistant-Human: A Human-Central Recommendation Framework with Large Language Models
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Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models
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Does synthetic data generation of llms help clinical text mining?
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One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems
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UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
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FLIP: Towards Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
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