Enhancing Large Language Model Based Sequential Recommender Systems with Pseudo Labels Reconstruction (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have been utilized in various studies, but their training sequences and text labels can alter their pre-trained weights, reducing their ability to construct and comprehend natural language sentences. |
| Approach: | They propose a reconstruction-based LLM recommendation model that harnesses the feature extraction capability of LLMs while preserving LLM’s sentence generation abilities. |
| Outcome: | The proposed model exploits the key features of both user and item pseudo-labels generated from user reviews while training on sequential data. |
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| Challenge: | Existing models for text-based recommendation lack data sparsity and flexibility to capture fluctuations in user preferences over time. |
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| Challenge: | Autoregressive decoders in large language models excel at capturing sequential behaviors for generative recommendations, but they lack graph-structured user-item interactions, which are widely recognized as beneficial. |
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| Challenge: | Existing large language models (LLMs) generate redundant output, which generates irrelevant information about the user’s preferences on candidate items from user behavior sequences. |
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