Recommendation method and system of efficient data analysis

A data analysis and recommendation method technology, applied in the direction of electronic digital data processing, special data processing applications, instruments, etc., can solve the problems of speeding up the recommendation, improve the conversion rate, improve user experience, solve algorithm optimization and traceability of recommendation results the effect of the problem

Inactive Publication Date: 2016-12-07
盛玲
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a scene-aware recommendation system that improves the sparse linear method, thereby solving the sparse data set problem and the attribution of the recommendation results at the same time, so that the key indicators such as the accuracy rate and recall rate of the recommendation results are significantly improved. Improvement, the recommendation speed is greatly accelerated

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  • Recommendation method and system of efficient data analysis
  • Recommendation method and system of efficient data analysis
  • Recommendation method and system of efficient data analysis

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Embodiment 1

[0022] Embodiment 1. Setting the user's rating of a certain item in different scenarios is the correction of the user's indiscriminate rating of the item scenario, and we introduce the offset model into the sparse linear method. This model is mainly used to estimate the user score Sijc, that is, the score given by user ui to item tj in scene c.

[0023] Firstly, the scene is modeled, and the scene model is represented by a binary vector c=. Assuming that all scenes can be expressed as {Time=weekend, Time=weekday, Location=school, Location=home}, then the scene vector c= means that the scene is at school on weekends.

[0024] Due to the addition of the scene model, the data set will become more sparse, and it is usually impossible to guarantee that the user has rated multiple items in the same scene. Therefore, based on the user's scene indiscriminate score, scene variables are introduced for correction.

[0025] R i，j，c =R i，j +∑D jlcl

[0026] Finally, the sparse linear...

Embodiment 2

[0027] Embodiment 2. In a supermarket, the method of the present invention is used to collect commodity information and consumer information, and consumer behavior is extracted and abstracted into a feature matrix W. Next, build the scene module, use multi-parameters to build the weather, date, shopping vehicle, and the number of people in the same party, and use the camera to capture the customer's purchased items in real time, and build the binary vector c= in real time. Perform real-time calculations on user features and scene vectors to get instant item recommendations. As shown in the attached figure, according to the instant item recommendation result, after filtering and recommendation explanation, the final push result is obtained and fed back to the user.

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Abstract

The invention relates to a recommendation method and system of efficient data analysis. Specifically, the synchronous real-time recommendation of a scene perception recommendation algorithm is realized through a grading module, a scene optimization module and a big data operation module. A scene model is introduced to correct a sparse linear method, and a recommendation engine based on memory calculation is imported, so that recommendation efficiency is greatly improved. Meanwhile, the problems of the algorithm optimization of a sparse data set and recommendation result abduction are solved. The system can be applied to the real-time recommendation of supermarket consumption. Compared with existing off-line recommendation, the system is characterized in that a user can timely receive relevant recommendations after selecting or purchasing corresponding products, and therefore, a conversion rate brought by the recommendation system is greatly improved. A scene model is imported to cause the user to obtain accurate recommendations under the certain scene under different scenes, and user experience is improved.

Description

technical field [0001] This application relates to a data mining technology, in particular to a method for analyzing consumption data of shopping malls and supermarkets, and a scene-aware recommendation system based on this method. Background technique [0002] Data mining refers to the process of revealing hidden, previously unknown and potentially valuable information from a large amount of data. It is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, database, data retrieval and other technologies to achieve the above goals. The existing data mining process uses one or several fixed data analysis algorithms to build a data analysis system. Since each algorithm has its own advantages and disadvantages, it often causes deviations between data analysis results, resulting in data analysis based on data analysis. It is difficult for upper-level business applications to make decisions. [0003] The recommendation system is a functiona...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 盛玲陶琎
Owner 盛玲
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