arXiv:2604.18800v2 Announce Type: replace-cross Abstract: We study online learning for new products on a platform that makes capacity-constrained assortment decisions on which products to offer. For a newly listed product, its quality is initially unknown, and quality information propagates through social learning: when a customer purchases a new product and leaves a review, its quality is revealed to both the platform and future customers. Since reviews require purchases, the platform must feature new products in the assortment ("explore") to generate reviews to learn about new products. Such
The proliferation of new products and the increasing sophistication of online platforms necessitate optimal strategies for product discovery and learning in dynamic market conditions.
This research provides a framework for platforms to efficiently identify high-quality new products, improving customer experience and optimizing resource allocation in capacity-constrained environments.
Platforms can move beyond heuristic approaches to optimal, data-driven exploration of new product offerings, fundamentally changing how new products are introduced and evaluated online.
- · E-commerce platforms
- · New product companies
- · Consumers (through better product discovery)
- · AI/ML algorithm developers
- · Inefficient platforms
- · Low-quality new products
Platforms will adopt more sophisticated algorithms to manage assortment decisions and product discovery, leading to more efficient market operations.
Increased efficiency in product discovery could accelerate innovation cycles as high-quality new products gain traction faster, while poor ones are deselected more quickly.
This optimized exploration could lead to greater consolidation of market power among platforms that effectively implement such systems, creating higher barriers to entry for competitors.
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Read at arXiv cs.LG