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In contrast to item IDs, user reviews contain rich semantic information about item characteristics and user experiences. Reviews explicitly describe aspects such as functionality, quality, and user satisfaction, which are crucial for understanding user preferences but cannot be captured by item identifiers alone.
Although several prior studies have explored incorporating review text into sequential recommendation, most of them treat reviews as auxiliary features rather than as an equal source of information. As a result, the joint modeling of behavioral signals and semantic signals within a unified sequential framework remains underexplored.
To address this gap, we propose HiSeR, a hybrid sequential recommender that jointly integrates item IDs and review semantics. HiSeR fuses ID embeddings with semantic representations extracted from pre-trained language models, and leverages a Transformer-based sequential encoder to capture user preference dynamics for next-item prediction.
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IEEE/ICACT20260275 Slide.03
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