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Distributed personalized recommendation method and system

A recommended method and distributed technology, applied in the field of distributed computing, can solve the problems of fast connection speed, low applicability, and high overhead on the Map side, and achieve the effect of improving connection efficiency, saving network transmission resources and input and output costs

Active Publication Date: 2014-08-20
UNIV OF SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The connection on the reduce side has high applicability, but the overhead is high, and the map side has fast connection speed and low applicability

Method used

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  • Distributed personalized recommendation method and system
  • Distributed personalized recommendation method and system
  • Distributed personalized recommendation method and system

Examples

Experimental program
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Embodiment 1

[0032] figure 1 It is a flow chart of a distributed personalized recommendation method provided by Embodiment 1 of the present invention. Such as figure 1 As shown, the method mainly includes the following steps:

[0033] Step 11: Establish a scoring set including user information, the user's scoring items and corresponding scoring values.

[0034] Before that, it is necessary to obtain user information, the user's rated items and corresponding rated values, and the user's unrated items.

[0035] Users generate various information by browsing the Internet or using clients. For example, users will leave personal information through registration, including gender, age, region, occupation, interests, etc.; The rating information of the music; the user listens to the music and generates the music and ratings for the music. For different network behaviors of users, the types of information generated by users are also different.

[0036] After analyzing the obtained above infor...

Embodiment 2

[0092] Figure 4 It is a schematic diagram of a distributed personalized recommendation system provided by an embodiment of the present invention. Such as Figure 4 As shown, the system mainly includes:

[0093] Scoring set building module 41, used to set up a scoring set that includes user information, the user's scoring items and corresponding scoring values;

[0094] Item scoring difference information calculation and writing module 42, used to calculate the arithmetic mean of all items of all users and the total number of times that the same item occurs to the scoring difference according to the set, and write the pre-built item to scoring difference table; wherein , the score collection and the item pair score difference table are all stored in an Hbase table;

[0095] Unrated item prediction scoring module 43, for utilizing the MapReduce mapping simplification model to connect the user information stored in the HDFS file system and the collection of unrated items ther...

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Abstract

The invention discloses a distributed personalized recommendation method and system. The method includes the steps of setting up a score set containing user information, user graded items and corresponding score values; calculating the arithmetic mean value of all item pair score differences of all users and the total number of appearance times of the same item according to the score set and writing the arithmetic mean value and the total number in a preset item pair score difference table, wherein both the score set and the item pair score difference table are stored in a Hbase list; linking user information stored in an HDFS file system with a set of items, not graded, of the users and the item pair score difference table for the first time through a MapReduce mapping and simplifying model; linking the first-time linkage result with the score set for the second time, and calculating forecast score values of the items, not graded, of the user through a forecast algorithm; giving recommendation to the users according to the sizes of the forecast score values. By means of the method and the system, network transmission resources and input and output expenses are saved, and linkage efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of distributed computing, in particular to a distributed personalized recommendation method and system. Background technique [0002] A personalized recommendation system is a system that recommends information or products of interest to users based on their personal information, interest characteristics, and purchase behavior. The recommendation system has three important modules: user modeling module, recommendation object modeling module, and recommendation algorithm module. The recommendation system matches the interest and demand information in the user model with the feature information in the recommended object model, and uses the corresponding recommendation algorithm to calculate and filter, find the recommended objects that the user may be interested in, and then recommend them to the user. [0003] Hadoop is an open source project, an open source distributed computing platform for big data proces...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/182
Inventor 王雷况亚萍夏磊张成晨
Owner UNIV OF SCI & TECH OF CHINA
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