Intelligent commodity recommending method based on word vector data driving

Abstract

The invention discloses an intelligent commodity recommending method based on word vector data driving. The method includes the following steps: data pre-processing, word vector generation, score prediction constructing, model training and score prediction. The method includes the following steps: using a word vector method to take user numbers, commodity numbers and commodity scores as semantic words, changing the semantic words to a sparse vector by conducting one-hot encoding, then multiplying the sparse vector and a weight matrix and mapping a high-dimension and sparse original vector to a dense, continuous and fixed and low-dimension feature space, then inputting the original vector to a deep model for carrying out training to obtain weight parameters of each layer of a model, predicting and scoring a new user's favor to the commodity by using a well-used model so as to complete intelligent recommendation of the commodity to the user. According to the invention, the method applies the word vector method which classifies texts to score prediction and commodity recommendation which is based on favor of the user of an e-commerce platform to the commodity. The method can ensures precision and provide better explanation.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and more specifically, relates to an intelligent recommendation method for commodities driven by word vector data. Background technique [0002] With the development of the Internet, e-commerce emerges as a new industry. E-commerce moves goods from physical stores to the virtual environment of the network, allowing users to shop without leaving home. Virtual shopping reduces costs for sellers and improves the shopping experience for buyers. But for online shopping users, the variety of products on the Internet brings troubles to selection. The way to solve this problem is to use recommendation system to recommend personalized items for users. [0003] Traditional personalized recommendation systems include collaborative filtering algorithm, KNN clustering algorithm, factor model, restricted Boltzmann machine, etc. These traditional methods are often not accurate enough to accom...

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These traditional methods are often not accurate e...

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