Item recommendation method and device and storage medium

Abstract

The embodiment of the invention discloses a item recommendation method and device and a storage medium, and the method comprises the steps: extracting item characteristics from a item which a currentuser is interested in; expanding the extracted item features to obtain target features; pulling an item according to the target feature to obtain a candidate item; extracting a user tag from the userportrait of the current user; calculating the similarity between the current user and the candidate item according to the extracted user tag and the item characteristics of the candidate item; and selecting a recommended target item from the candidate items according to the similarity between the current user and the candidate items. According to the embodiment of the invention, the algorithm complexity can be reduced, and the recommendation accuracy is improved.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of the Internet, in particular to an item recommendation method, device and storage medium. Background technique [0002] With the development of Internet technology, more and more users obtain interested items (items) through the Internet, such as: articles, information, items, etc. In order to improve user stickiness, item providers usually recommend to users the items that users may be interested in. How to determine the items that the user may be interested in and recommend the item to the user has become an important issue for technicians to study. [0003] The current recommendation system solves the recommendation problem of user-interested items, and there are usually two types of methods: [0004] The first category: user-based collaborative filtering (user-based Collaborative Filtering, referred to as UserCF) algorithm, the basic idea of ​​this method is based on the user...

AI Technical Summary

Problems solved by technology

[0006] The User CF algorithm needs to establish the scoring matrix of users (users) and items based on the user's click or voting results. Since the number of users is usually relatively large, the matrix calculation scale is huge, and the training process requires a lot of cost; in the Item CF algorithm , because the amount of items is usually not very large, it is more convenient to calculate, but the Item CF algorithm needs to describe the semantics of items in multiple dimensions to get better recommendation results, which requires a lot of feature engineering work, high complexity, and tuning difficulty

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