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Abstract User-driven customization is a particular design paradigm where customers act as co-designers to configure products based on their needs. However, due to insufficient product usage experience, customers may design a product incompatible with their environment and needs. Such incompatibility can negatively affect the performance of some customized features or even cause product failure. As a result, customers may hesitate to customize products because additional complexities and uncertainties are perceived. Product usage context (PUC), as all the environment and application factors that affect customer needs and product performance, can be used to facilitate customer co-design in user-driven customization. Identifying individual customer’s PUC can help customers foresee potential design failures, make more holistic design decisions, and be confident with their designs. Against the background, this paper proposes a PUC knowledge graph (PUCKG) construction method using user-generated content (UGC). The proposed method can convert crowdsourced corner cases into structured PUCKG to support personal PUC prediction, summarization, and reasoning. A case study of robot vacuum cleaners is conducted to validate the efficacy of the proposed method.
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Stored only when both papers are in the corpus — 23 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.