Meng Jian

2 accepted papers

2026

LLM-Summarized Interest Distillation for Multimedia Recommendation

Meng Jian, Zhuoyang Xia, Haolun Fan, Lifang Wu

IJCAI 2026

Recommendation system technology faces the fundamental challenge of data sparsity. Although incorporating auxiliary textual data works to enrich interactions, lengthy and unstructured item descriptions often fail to depict user interests, as they primarily describe item attributes. Furthermore, the

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2026

MSR-Rec: Multi-Step Reasoning-Enhanced LLM for Sequential Recommendation

Tuo Wang, Meng Jian, Ge Shi, Lifang Wu, Yashen Wang

AAAI 2026technical

Sequential recommendation has become indispensable in modern digital services. Prevalent recommendation techniques formulate the recommendation task with a language instruction fed into large language models (LLMs) to generate recommendations. However, the implicit interaction scenario of recommenda

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