House resource recommendation method and device based on deep learning and readable storage medium

Abstract

The invention discloses a house resource recommendation method and device based on deep learning and a readable storage medium. The house resource recommendation method comprises the steps: obtaininga feature city set according to city demand factors of a user; selecting an intentional target city set by the user in the feature city set; recommending a part of housing resources meeting the user housing resource demand factors in the target city set to the user; enabling a user to make selection, and obtaining a selected house resource set; carrying out deep learning on the characteristic factors of the house resources in the selected house resource set to obtain a feature set of house resources subjected to deep learning processing, wherein the feature set of the house resources can reflect the real demand of a user on the house resources; and recommending the house resources meeting the feature set in a feature city house resource set to the user, thereby helping the user to select satisfactory cities and house resources. Furthermore, the technical problem of mismatching of uncertain user house resource recommendation for the target city is solved, and the technical effects of recommending the house resources according to the real demands of the user and improving the house resource recommendation efficiency are achieved.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a deep learning-based house source recommendation method, device and readable storage medium. Background technique [0002] With the continuous development of computer technology, it has been widely used in various fields. For example, in the field of real estate transactions, Internet-based real estate transaction platforms have developed rapidly. [0003] In the existing real estate trading platforms, the user first selects the target city, and then recommends a suitable house source to the user from the house source library in the target city. [0004] However, in the process of implementing the technical solutions in the embodiments of the present application, the inventors of the present application found that the above-mentioned technology has at least the following technical problems: [0005] In the above-mentioned real estate trading platforms, for users whose...

AI Technical Summary

Problems solved by technology

[0008] In this way, on the one hand, the house source recommendation method collects samples from the two dimensions of house source and city, and the sample can better meet the needs of users to buy houses; on the other hand, through deep learning processing of samples, it can objectively screen Find out the real needs of users, so as to help users choose satisfactory cities and housing sources; furthermore, it solves the technical problem that the user’s housing source recommendation does not match the user’s uncertain target city, realizes the recommendation of housing sources according to the real needs of users, and improves The technical effect of listing recommendation efficiency

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