Directed graph association rule-based music cloud recommendation algorithm

A recommendation algorithm and directed graph technology, applied in the field of multimedia analysis, can solve the problems of algorithm performance degradation, the impact of search algorithm performance data set density, etc., to achieve the effect of reducing space

Inactive Publication Date: 2016-06-29
BEIJING JIAOTONG UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, when we reduce the support to a very low level or the data objects we deal with are dense data sets, such as: biological genetic data, communication data, etc

Method used

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  • Directed graph association rule-based music cloud recommendation algorithm
  • Directed graph association rule-based music cloud recommendation algorithm
  • Directed graph association rule-based music cloud recommendation algorithm

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Embodiment Construction

[0054] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0055] The music cloud recommendation algorithm based on directed graph association rules of the present invention comprises the following steps:

[0056] A: Create a database

[0057] The process of establishing a database includes: establishing a relational database, an Hbase database (distributed database based on column storage), and migrating from a relational database to an Hbase database;

[0058] A1 establishes a relational database: taking the music feature tuple as an element, and using a binary phenotype relational model database to represent the music feature data and the connection between the data;

[0059]A2 Establish Hbase-type database: establish a distributed Hbase-type database based on column storage, so that the number of hardware can be easily changed later and error compatibility can be improved;

[0060] A3 migration from relational ...

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Abstract

The invention relates to a directed graph association rule-based music cloud recommendation algorithm. According to the directed graph association rule-based music cloud recommendation algorithm, data modeling analysis is carried out on music cloud operation data so as to provide technological means and services for the music market analysis and aid decision making of commercial operation departments; by mining the maximum frequent item sets, the mining efficiency can be effectively improved and the scale of a problem solution can be effectively reduced; and the algorithm adopts a depth-first search method, nodes in node sets of directed item set graphs are selected as initial nodes to carry out search, and judgement is carried out on the frequent extension sets of each item set, so that the problem space can be effectively reduced, the number of candidate sets of the to-be-checked maximum frequent item sets can be decreased, and the efficiency of the algorithm can be effectively improved. The efficiency of the algorithm can be further optimized and improved by adjusting the arrangement sequence of the node sets of the directed item set graphs, adjusting the generation sequence of directed edges and designing an effective storage manner for the maximum frequent item sets.

Description

technical field [0001] The invention relates to the technical field of multimedia analysis, in particular to a music cloud recommendation algorithm based on directed graph association rules. Background technique [0002] Traditional frequent itemset discovery algorithms, such as: Apriori (based on priori algorithm), Partition (based on partition algorithm), DHP (based on hash algorithm), Sample (based on sample interpolation algorithm) and other algorithms, are found to be hidden in the data set When the processed data object is a sparse set or the minimum support setting is relatively high, this type of algorithm has a good execution effect. However, when we reduce the support to a very low level or the data objects we deal with are dense data sets, such as biological genetic data, communication data, etc., the performance of this type of algorithm decreases very quickly. Even the performance of depth-first search algorithms can be affected by the density of the dataset. ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/635
Inventor 柯新生
Owner BEIJING JIAOTONG UNIV
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