Crowd sourced trends and recommendations

A computer program product, the computer program product comprising:

• a non-volatile computer readable storage medium having program instructions embodied therewith, the program instructions executable by a number of processors to cause the computer to perform the steps of:
• receiving, from a number of data sources, data regarding choices of people at a number of specified events and public places;
• determining, with a number of clustering algorithms, a number of trend clusters according to the data received from the data sources cross-referenced to defined event types and place types;
• receiving, from a number of registered customers, customer profile data and preferences;
• determining, with the number of clustering algorithms, a number of customer clusters according to the customer profile data and preferences;
• deriving correlation rules between the trend clusters and the customer clusters;
• and
• sending, according to the correlation rules, a number of trend predictions and clothing recommendations to a user regarding a number of specified events or time frames,
• wherein the instructions for sending trend predictions and clothing recommendations further comprise instructions for:
• receiving, from a number of sensors, information about a number of clothing items in which the sensors are incorporated that are being tried on by a registered customer;
• determining in real time, according to the correlation rules, a number of clothing recommendations for the customer according to the correlation rules;
• and
• displaying the clothing recommendations to the customer through an interface.

A system, comprising:

• a bus system;
• a storage device connected to the bus system, wherein the storage device stores program instructions;
• and
• a number of processors connected to the bus system, wherein the number of processors execute the program instructions to:
• receive, from a number of data sources, data regarding choices of people at a number of specified events and public places;
• determine, with a number of clustering algorithms, a number of trend clusters according to data received from the data sources cross-referenced to defined event types and place types;
• receive, from a number of registered customers, customer profile data and preferences;
• determine, with the number of clustering algorithms, a number of customer clusters according to the customer profile data and preferences;
• derive correlation rules between the trend clusters and the customer clusters;
• send, according to the correlation rules, a number of trend predictions and clothing recommendations to a user regarding a number of specified events or time frames;
• receive feedback about fashion choices made by the user;
• update the trend clusters and correlation rules according to the feedback;
• and
• refine future trend predictions and recommendations according to the feedback.