Spark-based big data hybrid model mobile recommending method

A hybrid model and recommendation method technology, applied in data processing applications, character and pattern recognition, instruments, etc., can solve problems such as consumer loss

Inactive Publication Date: 2016-10-26
CENT SOUTH UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This process of browsing a large amount of irrelevant information and products will undoubtedly lead to the loss of consumers who are submerged in the problem of information overload.

Method used

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  • Spark-based big data hybrid model mobile recommending method
  • Spark-based big data hybrid model mobile recommending method
  • Spark-based big data hybrid model mobile recommending method

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Embodiment Construction

[0046] Further illustrate the present invention with reference to accompanying drawing;

[0047] A mobile recommendation method based on Spark's big data hybrid model

[0048] Step A, the features extracted according to the user’s operation behavior data on the mobile terminal are as follows: whether the user and the product are in the same area, the number of times the user browses, the browsing interval, whether to save, whether to add to the shopping cart, the sales volume of the item, and the brand name of the item Sales volume, item popularity, brand conversion rate, user conversion rate, user's rating of the product, whether to buy it in the end, etc.; build a random forest model input data set D1 {u, p, ...} multi-attribute group, u represents the user ID , p represents the product ID, other characteristic attribute items represented by . except the user’s rating of the product; construct the ALS collaborative filtering model input data set D2{u,p,s} triple attribute gr...

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Abstract

The invention puts forward a Spark-based big data hybrid model mobile recommending method comprising four steps as follows: firstly, getting a user's commodity purchase data at a mobile end; secondly, extracting user historical data from a database and importing the user historical data to an HDFS, and extracting features, such as user behavior features, brand features, user's personal consumption features and cross features; thirdly, packaging a hybrid model on a Spark platform using an RDD operator, and embedding the model interface into a big data platform for calling; and fourthly, calling the hybrid model interface to extract feature data, setting training parameters of the model, and training the hybrid model. The model is estimated using a test data set and optimized, the trained hybrid model is saved, and relevant recommendation is made. The method can effectively improve the efficiency of recommendation under the condition of a larger amount of data and higher data sparseness.

Description

technical field [0001] The invention relates to the technical field of mobile recommendation methods in a big data environment Background technique [0002] In recent years, with the rapid development of the mobile Internet and O2O models, the data generated by the mobile terminal has continued to increase, resulting in the traditional single computer being unable to store and process such a large amount of data, so various big data storage and computing platforms came into being at this time pregnancy. But today the most used big data storage platform is HDFS, and the most used computing framework is Spark. [0003] Personalized mobile recommendation is based on the user's interest characteristics, operation behavior, and operation location, and recommends information and products that the user is interested in to the user. With the continuous expansion of e-commerce O2O scale, the number and types of products are increasing rapidly, and customers need to spend a lot of t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06K9/62
CPCG06Q30/0631G06F18/254G06F18/214
Inventor 邓晓衡宋凯沈海澜吴佳祺曾德天
Owner CENT SOUTH UNIV
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