Probabilistic clustering of an item

A system comprising:

• an access module, implemented using at least one hardware processor, configured to:
• access item data that corresponds to an item available for sale and inclusive of an item variable that represents information describing an attribute of the item, the item variable being a binary variable that represents an occurrence of a term in textual data descriptive of the item available for sale;
• and
• access a vector that is representative of a cluster of items having attributes corresponding to the attribute of the item, the vector representing a product of which the item is a specimen, the vector including a probability distribution that is pertinent to the cluster of items and represented as a plurality of probability parameters that include a probability parameter modeling the binary variable that represents the occurrence of the term in the textual data descriptive of the item available for sale;
• a processor configured by a probability module that configures the processor to calculate a result based on the binary variable that represents the occurrence of the term in the textual data descriptive of the item available for sale including calculating a logarithm based on the probability parameter modeling the binary variable and calculating the result based on the logarithm, the result representing a likelihood that the item is a specimen of the product represented by the cluster of items having attributes corresponding to the attribute of the item and represented by the vector;
• a determination module to determine that the item is included in the cluster based on the result;
• and
• a recommendation module to present a recommendation of the item available for sale to a potential buyer of the item, the recommendation indicating that the item is the specimen of the product represented by the cluster of items.

A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

• accessing item data that corresponds to an item available for sale and inclusive of an item variable that represents information describing an attribute of the item, the item variable being a binary variable that represents an occurrence of a term in textual data descriptive of the item available for sale;
• accessing a vector that is representative of a cluster of items having attributes corresponding to the attribute of the item, the vector representing a product of which the item is a specimen, the vector including a probability distribution that is pertinent to the cluster of items and represented as a plurality of probability parameters that include a probability parameter modeling the binary variable that represents the occurrence of the term in the textual data descriptive of the item available for sale;
• calculating a result based on the binary variable that represents the occurrence of the term in the textual data descriptive of the item available for sale including calculating a logarithm based on the probability parameter modeling the binary variable and calculating the result based on the logarithm, the result representing a likelihood that the item is a specimen of the product represented by the cluster of items having attributes corresponding to the attribute of the item and represented by the vector;
• determining that the item is included in the cluster based on the result;
• and
• presenting a recommendation of the item available for sale to a potential buyer of the item, the recommendation indicating that the item is the specimen of the product represented by the cluster of items.