How does the Staytuned Basic Recommender decide on sizes for borderline or larger size scenarios?
Overview
The Basic Apparel Recommender employs sophisticated algorithms to suggest the best product size for customers based on their provided body measurements. Key influencing factors include the customer’s measurements, product design, and settings such as stretch, ease, and fit type preferences. The system ensures a comfortable and personalized fit while addressing specific sizing scenarios.
Decision-Making for Borderline Sizes
When a customer’s body measurements fall between two sizes (e.g., XS and S), the Basic Apparel Recommender compares the input measurements against the product's intended dimensions. In such cases, the recommender selects the size closest to the customer’s measurements, prioritizing a comfortable fit. If both options are equally acceptable, the system generally favors the smaller size, ensuring it neither fits too loosely nor compromises comfort.
Logic Behind Recommending Larger Sizes
If the recommender detects that the input body measurements are very close to or exceed the dimensions of a particular size, it may recommend a larger size to ensure comfort. This approach is influenced by settings such as:
Stretch Factor: Determines how much flexibility the fabric allows.
Ease Settings: Ensures there is adequate space around body measurements for comfort.
Fit Type: Accounts for whether the product has a conservative or relaxed fit design.
For example, if the body measurements are close to the limits of a smaller size and the product lacks sufficient stretch or ease, the system avoids recommending overly tight sizes and opts for a larger one.
Key Influencing Factors
Several factors contribute to the system’s recommendations:
Body Measurements: Input measurements provided by the customer.
Product Dimensions: Intended sizing and dimensions of the product.
Ease and Stretch Settings: These ensure the recommended size is not uncomfortably tight.
Fit Preferences: Information on how snugly or loosely the product is meant to fit (e.g., relaxed versus fitted styles).
By integrating these variables, the Basic Apparel Recommender ensures that customers receive a size recommendation that combines precision with comfort.