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TV shopping commodity recommendation method based on classification algorithm

A technology for TV shopping and product recommendation, which is applied in the field of TV shopping product recommendation based on classification algorithm, achieves the effects of comprehensive feature work, improved operability, and alleviation of cold start problems

Inactive Publication Date: 2016-11-16
ZHEJIANG UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

However, there are few examples of applying recommendation algorithms to TV shopping, mainly because of the particularity of the scene

Method used

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  • TV shopping commodity recommendation method based on classification algorithm
  • TV shopping commodity recommendation method based on classification algorithm
  • TV shopping commodity recommendation method based on classification algorithm

Examples

Experimental program
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Embodiment Construction

[0025] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0026] In this example, we disassemble a product recommendation system based on classification algorithms into three interrelated modules: feature engineering module, model training module, and prediction module, and finally realize personalized product recommendation based on user interests; among them:

[0027] The content involved in the feature engineering module includes three parts: data preprocessing, feature extraction and feature transformation. Data preprocessing mainly fills missing values ​​through statistical simulation, and screens and removes outliers; on the basis of data preprocessing, extracts predetermined features; feature transformation mainly expands label data, and some continuous data Discretize.

[0028] The model training module...

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Abstract

The invention discloses a TV shopping commodity recommendation method based on a classification algorithm. The TV shopping commodity recommendation method comprises the steps of: converting a prediction problem into a classification problem by utilizing logistic regression and a random forest, namely, predicting that purchasing behaviors of a user about a commodity can be divided into two categories: purchasing and not purchasing; extracting features from commodity information, user information and a user behavior record as input, and taking a prediction score of the user as output, thereby forming a function; and training a model by adopting a linear regression method, and converting the problem into a training classifier problem. The TV shopping commodity recommendation method does not carry out prediction and calculation on the basis of a heuristic rule, but carries out prediction on the basis of data analysis and statistics as well as machine learning for training model; and new users and new commodities can be calculated and predicted quickly as long as the model is trained.

Description

technical field [0001] The invention belongs to the technical field of electronic commerce, and in particular relates to a method for recommending TV shopping commodities based on a classification algorithm. Background technique [0002] As an important research direction of big data technology, the recommendation system has been widely used in various fields of the Internet. The main business model of the traditional TV shopping industry is: use TV as the main promotion channel to manually display products on TV continuously. One-to-one merchandising (outbound calls). With the rapid development of the Internet, more and more users tend to shop online, but TV shopping is still highly attractive to middle-aged and elderly people. [0003] With the increase in the number of products and members, it is becoming more and more difficult for agents to choose which products to recommend to users, especially for inexperienced junior agents. Moreover, in today's era of information...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02H04N21/254
CPCG06Q30/0282H04N21/2542
Inventor 吴健邱奇波顾盼叶刚峰谢志宁邓水光李莹尹建伟吴朝晖
Owner ZHEJIANG UNIV
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