Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants
arXiv:2606.24194v1 Announce Type: cross Abstract: Conversational product search assistants offer a more expressive, natural, and interactive alternative to traditional keyword-based product search. With limited screen space, showing only a few items increases the need for precise preference elicitation, which can prolong conversations, leading to user frustration and session abandonment. Conversely, rushing to recommend items without a clear understanding of preferences risks poor matches and a degraded user experience. We present Dialogue to Discovery (D2D), an attribute-oriented preference e
The rapid advancement in large language models and conversational AI makes sophisticated preference elicitation for product search both feasible and a critical bottleneck for user experience.
Improving conversational product search directly enhances e-commerce efficiency and user satisfaction, potentially shifting how consumers interact with online shopping platforms.
Product search will become more intuitive and less reliant on explicit keyword input, moving towards natural language dialogue for discovery.
- · E-commerce platforms
- · AI software developers
- · Consumers
- · Online retailers
- · Traditional keyword search engines
- · Inefficient sales funnels
More efficient and satisfying online shopping experiences driven by AI assistants.
Increased conversion rates and reduced abandonment for e-commerce sites employing advanced conversational AI.
Conversational interfaces becoming the dominant mode of interaction across various digital services, beyond just product search.
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