Abstract: One or more processors deconstruct, by utilizing natural language processing (NLP), a text product review into multiple n-grams, where each of the multiple n-grams is a sequence of words from the text product review, and where each of the multiple n-grams is a review of a particular component from the multiple components of the product. The processor(s) generate a component numeric rating value (CNRV) for each of the multiple n-grams using machine learning techniques such as collaborative filtering, and store CNRVs for the multiple components of the product together with identifiers of particular components of the product. The processor(s) receive a request for a particular CNRV for the particular component of the product, retrieve the particular CNRV, and then transmit the particular CNRV to a requesting device of the request in order to adjust an operational feature of the requesting device.

Abstract: At least one financial information for a user is received. A machine learning model is determined. The machine learning model is determined based on the user. A personality of the user is determined based on the machine learning model and the at least one financial information for the user. A recommendation is provided based on the determined personality of the user.