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To conclude, we propose HiSeR, a hybrid sequential recommendation framework that jointly integrates item ID–based collaborative signals and review-based semantic information.Ħħ
By combining a Hybrid Item Encoder with a Transformer-based Sequential Encoder, HiSeR effectively captures both short-term and long-term user preference patterns.
Through extensive experiments on two Amazon review datasets, we show that HiSeR consistently outperforms ID-based, review-based, and hybrid baseline methods across multiple evaluation metrics.
In addition, HiSeR incorporates an auxiliary rating regression objective, allowing the model to leverage explicit feedback and further improve representation quality and ranking performance.
Finally, future work includes extending HiSeR to multimodal settings, modeling temporal dynamics more explicitly, and applying large language models to enhance review semantic encoding.
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IEEE/ICACT20260275 Slide.15
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