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CTR prediction plays a vital role in recommender systems.
Cache memories
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FLIP: Towards Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
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A survey on large language models for recommendation
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A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, and Silvia Milano. 2024 · 2024
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Foundation Models for Recommender Systems: A Survey and New Perspectives
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ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction. In Proceedings of the ACM on Web Conference 2024 (WWW ’24) . 3319–3330
Jianghao Lin, Bo Chen, Hangyu Wang, Yunjia Xi, Yanru Qu, Xinyi Dai, Kangning Zhang, Ruiming Tang, Yong Yu, and Weinan Zhang. 2024a · 2024
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ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation. In Proceedings of the ACM on Web Conference 2024 (WWW ’24) . 3497–3508
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Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
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Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
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