Latent Product Clustering for Volatile Item Recommendations

The patent introduces latent products as an intermediary layer between volatile items and user preferences. Each volatile item is projected onto a latent product space where similar items cluster together, forming a bridge that connects sparse item data with user preference patterns. This intermediary structure enables accurate recommendations by translating high-dimensional sparse item data into a compressed latent space that captures essential product characteristics.

The system creates copies of volatile items in the latent product space. Instead of directly recommending volatile items with their sparse and unique descriptions, the system generates representative copies in the form of latent product vectors that capture the essential characteristics. These latent product copies serve as proxies for the original volatile items, enabling efficient and accurate recommendation without directly handling the sparsity of original item data.

The patent transforms the parameter representation of volatile items from their original unstructured descriptions into structured latent product vectors. By changing the parameter space from raw text descriptions to compressed numerical vectors in latent space, the system enables efficient similarity measurement using standard distance metrics. This parameter transformation maintains adaptability to diverse inventories while solving the difficulty of measuring similarity in unstructured data.

The system projects volatile items from their original high-dimensional unstructured description space into a lower-dimensional latent product space. This dimensionality change transforms difficult-to-measure unstructured data into a compact vector representation where similarity can be efficiently detected using geometric distance measures. The latent space provides another dimension for representing items that is more amenable to mathematical operations and similarity detection.