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Recommendation method in combination with frequent item set and deep learning under big data environment

A frequent itemset and deep learning technology, applied in the field of user recommendation, can solve the problems that the innovation speed of computing power cannot keep up with the growth rate of data volume, and it is difficult to handle massive data processing tasks, so as to achieve accurate user recommendation and overcome computing bottlenecks.

Inactive Publication Date: 2015-12-23
NANJING YOUZU INFORMATION TECH CO LTD
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Problems solved by technology

[0003] Traditional computing methods are not suitable for processing massive data, because the innovation speed of computing power of a single machine is far behind the growth rate of data volume

Method used

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  • Recommendation method in combination with frequent item set and deep learning under big data environment

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Embodiment Construction

[0014] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0015] The recommendation method combining frequent itemsets and deep learning under the big data environment of the present invention comprises the following steps:

[0016] Step 1: Collect user behavior records, and use the MapReduce parallel computing model to mine frequent itemsets in user behavior records.

[0017] For each user User i , to record the items that the user has followed recently if , and record it in database D.

[0018] The user User i , refers to the i-th customer of the enterprise using this recommendation method, who usually pays attention to one or more products of the enterprise Item if , Item if Refers to the user User i The jth product concerned, where Item ij ∈ User i , these products express user preferences. The structure of the database D is a two-dimensional table, each row is a two-tuple, and the content is (User i ,...

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Abstract

The invention discloses a recommendation method in combination with a frequent item set and deep learning under a big data environment. The recommendation method includes the following steps that behavior records of a user are collected, and a MapReduce parallel computation model is used for mining the frequent item set from the behavior records; a deep learning network is established, and the frequent item set is used for training the network; when recommendation is needed for the user, the behavior records of the user are collected as input, the established deep learning network is used for computation, and items are selected to be recommended to the user, wherein results of the items are larger than a threshold value. Based on the MapReduce parallel computation model, frequent item set mining can be efficiently performed inside a distributed system, with the frequent item set being a sample, the deep learning network is established, trained and used for recommendation, and compared with the mode that the frequent item set and a traditional neural network are directly utilized, recommendation can be performed for the user more accurately.

Description

technical field [0001] The invention relates to the technical field of user recommendation in a big data environment, in particular to a method for realizing user recommendation by mining frequent item sets in a distributed system and performing deep learning on the results. Background technique [0002] With the popularization of the Internet, we have entered the era of big data in units of PB, which has four characteristics: large amount, high speed, variety, and value. In the era of big data, data is wealth, how to fully mine user behavior records and make recommendations to users has become a key technology. The recommendation algorithm can predict the user's interests by analyzing historical data when the user has no clear needs, and actively provide information that the user may be interested in. The recommendation algorithm can not only help users quickly locate their needs among a large number of products, but also help merchants formulate targeted sales plans. [...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06N3/08
CPCG06F16/951G06N3/088
Inventor 陈礼标
Owner NANJING YOUZU INFORMATION TECH CO LTD
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