arXiv:2606.11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck. While pre-trained large language models (LLMs) can provide rich semantic representations, existing approaches only rely on static encoding of fixed attributes, overlooking the crucial role of target audiences in defining item identity. Moreover, the semantic space struggles to reflect actual user behavior, resulting in a significant gap between se
The proliferation of LLMs creates new opportunities and challenges for refining recommendation systems beyond static attribute encoding. This research addresses the current bottleneck of item representation quality.
Improved sequential recommendation models directly impact e-commerce, content platforms, and targeted advertising, driving revenue and user engagement. This advancement enhances the practical application of AI in consumer-facing services.
Recommendation systems will move towards more dynamic, audience-aware item representations, potentially leading to more accurate and personalized user experiences. The gap between semantic space and actual user behavior in recommendations could narrow.
- · E-commerce platforms
- · Streaming services
- · Generative AI model developers
- · Recommendation system engineers
- · Platforms using static recommendation models
- · Advertising firms relying on broad segmentation
Enhanced personalization in online experiences across various platforms.
Increased competition among platforms to leverage advanced AI for user retention and conversion.
Potential for new ethical considerations and regulatory scrutiny regarding highly personalized and potentially manipulative recommendations.
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