Car recommendation method and apparatus

Abstract

Embodiments of the invention disclose a car recommendation method and apparatus. The method comprises the steps of obtaining dimensional data of car models in car vertical websites, wherein the dimensional data comprises dimension evaluation scores; preprocessing the dimensional data to enable the evaluation scores to be distributed in a preset interval range; clustering the preprocessed dimensional data to obtain clustering clusters of different car models, wherein a clustering cluster number is determined according to dimensional information in the obtained dimensional data of the car models; obtaining dimensional information specified by a user and according to the dimensional information specified by the user, screening the obtained clustering clusters; and sorting all the car models in the clustering clusters in the selected clustering clusters, and displaying a sorting result to the user. Convenient and efficient car product recommendation can be provided for the user.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of data processing, and in particular to a car recommendation method and device. Background technique [0002] At present, there are two traditional methods for recommending car purchases. The first is the recommendation method based on professionals, which is to collect user needs, such as price, brand, model and other information, and then familiarize yourself with the car. Professionals in the market give several candidates based on personal experience. The second is the method of self-learning by users. According to their own needs, car purchase users compare and observe on various automobile vertical websites and different automobile 4S stores, and then choose the model that they think is reasonable. [0003] Since the general public knew little about cars in the past few years, the first professional-based recommendation method once became the mainstream method of obtaining a car...

AI Technical Summary

Problems solved by technology

Another problem is that due to the characteristics of personal preferences, different professionals will lead to the same user needs, and the candidate models given by different professionals are different, which cannot give users efficient recommendations.

[0004] For the second method of learning by users, although with the rapid development of the information age, the threshold for users to learn new domain knowledge has become lower and lower, but there are still certain technical barriers between different domain knowledge. The field of science and engineering is even worse. Because the automotive field involves many fields, which contains a lot of professional and technical knowledge, the learning cost of ordinary people without background is relatively high

Secondly, due to the energy and time constraints of each user, the car models that can be learned, the materials that can be learned, and the data that can be observed in 4S stores are all limited, so there will be some incomplete data samples, resulting in some high-quality The model is not shortlisted, that is, the selection made on the limited sample data sometimes ignores the high-quality models outside the sample

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