| CPC G06F 16/24578 (2019.01) [G06F 16/288 (2019.01); G06F 16/9535 (2019.01); G06N 20/00 (2019.01)] | 18 Claims |
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1. A method, comprising:
receiving a request for an automobile selection;
accessing a first plurality of tags related to a first automobile make and model of a plurality of automobile make and models, wherein the first plurality of tags are based on:
a term frequency-inverse document frequency (TF-IDF) model applied to a plurality of terms in a data source comprising a corpus of automobile reviews to compute a respective score for each of the terms, wherein the respective scores of a first subset of the plurality of terms exceed a threshold score; and
an aggregation of the terms of the first subset, wherein:
each tag of the first plurality of tags is associated with a respective plurality of probability distributions generated by the TF-IDF model, wherein each probability distribution associated with one of a plurality of vehicular features, the plurality of vehicular features comprising one of a vehicle component or an operating characteristic of the first automobile make and model, and wherein the first plurality of tags are further based on the respective plurality of probability distributions, and
the aggregation comprises a plurality of documents, each of the plurality of documents corresponding to a different one of the plurality of vehicular features;
returning a first tag of the first plurality of tags as a first suggestion responsive to the request based on:
a relationship between the first tag of the first plurality of tags and a second tag of a second plurality of tags, and
the first tag having a higher score than the second tag; and
updating, by the TF-IDF model based on a selection of the first automobile make and model, the plurality of probability distributions of each tag of the first plurality of tags.
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