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IEEE/ICACT20260275 Slide.04        [Big Slide]	Oral Presentation
	Based on these observations, in this paper We introduce HiSeR, a unified hybrid sequential recommendation framework that jointly leverages user interaction sequences and textual reviews within a single model. • We design a Hybrid Item Encoder that fuses item ID with semantic representations from pre-trained language models, and integrate these enriched item embeddings into a Transformer-based sequential encoder to effectively capture both short-term transitions and global contextual dependencies in user preference dynamics. • We conducted extensive experiments on two Amazon Re view datasets to validate the effectiveness and robustness of HiSeR, showing consistent performance improvements in multiple evaluation metrics.

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