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Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights.

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A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models., , , , , and . AAAI, page 3984-3991. AAAI Press, (2021)Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness., , , , , , , , and . CoRR, (2024)Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors., , , , , , , and . SIGIR, page 2311-2315. ACM, (2024)StackRec: Efficient Training of Very Deep Sequential Recommender Models by Layer Stacking., , , , , , , and . CoRR, (2020)Learning Transferable User Representations with Sequential Behaviors via Contrastive Pre-training., , , , and . ICDM, page 51-60. IEEE, (2021)Exploring evolution-aware & -free protein language models as protein function predictors., , , , , , , and . NeurIPS, (2022)Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation., , , and . SIGIR, page 1469-1478. ACM, (2020)CmnRec: Sequential Recommendations with Chunk-accelerated Memory Network., , , , and . CoRR, (2020)fBGD: Learning Embeddings From Positive Unlabeled Data with BGD., , , , , , and . UAI, page 198-207. AUAI Press, (2018)A Generic Network Compression Framework for Sequential Recommender Systems., , , , , and . SIGIR, page 1299-1308. ACM, (2020)