Identifying Popular Products at An Early Stage for Apparel Industry
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• Proposed a new indicator called AW Sales (Average Weekly Sales in Main Sales Period) to measure the popularity of a product, which is unaffected by the differences in store traffic, number of stores with initial stock, discounts and length of time the product has been launched.
• Constructed novel features such as the longest increasing subsequences derived from the sequence of weekly adjusted sales volume of product k across all stores within a typical region.
• Built a product popularity classification model for apprel field with LambdaMart ranking model, which is the first time the ranking algorithm has been applied to the sales prediction field. achieved a prediction accuracy of 78.9%, and identifies fast-selling products 17 days earlier than rule-based method.