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Mixed recommendation method based on factorization condition limitation Boltzmann machine

A restricted Boltzmann machine and hybrid recommendation technology, applied in special data processing applications, instruments, electrical digital data processing, etc.

Inactive Publication Date: 2013-09-25
FOCUS TECH +1
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  • Abstract
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Problems solved by technology

[0006] Aiming at the problem that traditional recommendation methods and systems cannot effectively combine user behavior data and item content attributes, the present invention uses item content attributes as information supplements to user behavior data, and provides an e-commerce website based on content and user behavior. Hybrid recommendation method and system for

Method used

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  • Mixed recommendation method based on factorization condition limitation Boltzmann machine
  • Mixed recommendation method based on factorization condition limitation Boltzmann machine
  • Mixed recommendation method based on factorization condition limitation Boltzmann machine

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Embodiment Construction

[0084] The present invention will be further described according to the drawings and specific embodiments below.

[0085] Such as figure 2 As shown, assuming that the number of items is 6, the ratings are divided into 5 levels, the rows of the matrix represent the ratings, and the columns represent the items. At the initial stage, all elements in the graph are 0, assuming that there is a target user behavior set { ,,}, so set the first row, first column, fourth row, fourth column, second row, fifth column of the target user rating matrix to 1, In addition, columns 2, 3, and 6 in the figure are all 0, indicating that the target user has not rated items 2, 3, and 6.

[0086] Such as image 3 As shown in the figure, the connection between the conditional layer and the hidden layer is a directed arrow, which means that the information transmission between the conditional layer and the hidden layer is unidirectional in the process of score prediction.

[0087] The present inven...

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Abstract

The invention provides a mixed recommendation method based on a factorization condition limitation Boltzmann machine. The method includes: constructing a target user feature vector; building a target user grading matrix set; building a target user training sample; building a factorization condition limitation Boltzmann machine model; grading and predicting to recommend results to a target user. The problems of user interest expression inaccuracy caused by pure recommendation based on content and data sparsity caused by recommendation based on behavior are effectively solved.

Description

technical field [0001] The invention relates to the field of information extraction, in particular to a hybrid recommendation method based on factorized conditionally restricted Boltzmann machines. Background technique [0002] With the development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity. At present, both information consumers and information producers have encountered great challenges: for information consumers, it is very difficult to find the information they are interested in from a large amount of information; It is also a very difficult thing for the information to stand out and attract the attention of the majority of users. Recommender systems are an important tool to resolve this contradiction. The task of the recommendation system is to associate users and information. On the one hand, it helps users find information that is valuable to them, and on the other ha...

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Application Information

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IPC IPC(8): G06F17/30G06Q30/02
Inventor 周洲李仁勇陈建国高志强陈翠翠归耀城全志斌
Owner FOCUS TECH
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