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Large-scale multi-objective optimization commodity recommendation method based on image coding

A multi-objective optimization and image coding technology, applied in the field of large-scale multi-objective optimization of commodity recommendation, can solve the problems of incomplete search and accuracy challenges of diverse commodities, and achieve rich diversity, rich coverage, and maintain diversity. Effect

Pending Publication Date: 2021-08-17
DALIAN UNIV OF TECH
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Problems solved by technology

[0004] However, the existing recommendation algorithms based on multi-objective optimization are not comprehensive enough to search for diversity, and fail to comprehensively consider user diversity, product diversity, product novelty and product coverage. One of the main reasons may be that the diversity of products Accuracy challenges posed by

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  • Large-scale multi-objective optimization commodity recommendation method based on image coding
  • Large-scale multi-objective optimization commodity recommendation method based on image coding
  • Large-scale multi-objective optimization commodity recommendation method based on image coding

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Embodiment Construction

[0076] Below in conjunction with accompanying drawing, the present invention is described in further detail:

[0077] see figure 1 and figure 2 , the present invention, the commodity recommendation method based on the large-scale multi-objective optimization algorithm of image coding, comprises the following steps:

[0078] Step 1: Obtain a dataset of recommended products for user u according to probability:

[0079] (1a) Calculate the similarity between two users:

[0080]

[0081] where r u with r v Respectively represent the rating vectors of user u and user v for all products, ||·|| 2 Represents a two-norm;

[0082] (1b) Obtain the data set of recommended products according to probability:

[0083] Set a probability threshold, preferably, α=0.8, if the random number is less than α, select the most similar items preferred by several users as the recommended dataset; otherwise, select the items preferred by the most dissimilar users as the recommended dataset . ...

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Abstract

The invention belongs to the technical field of commodity recommendation, and discloses a large-scale multi-objective optimization commodity recommendation method based on image coding. The method comprises the following steps: firstly, obtaining a data set of recommended commodities for a user according to probability, then coding the commodities based on commodity images, then constructing a target function based on recommendation accuracy and commodity diversity, then generating an initial population based on a non-dominated sorting and crowding distance method, and executing a memetic evolution system based on variable statistical division to optimize the population, and finally recommending the user according to the coding similarity. According to the method, a large-scale multi-objective optimization algorithm based on image feature modeling is adopted for optimization, and articles with similar features are selected from recommended commodities according to an optimization result for recommendation, so that the recommendation accuracy of diversified commodities can be improved.

Description

technical field [0001] The invention belongs to the technical field of commodity recommendation, and more specifically relates to a commodity recommendation method based on large-scale multi-objective optimization of image coding. Background technique [0002] While the development of the Internet has brought great convenience to human life, it has also brought about the problems of information explosion and information overload. For users, how to use limited energy to find the information they are interested in in massive data has become a new problem that needs to be solved urgently. To alleviate this problem, recommender systems were invented. By collecting the user's behavioral characteristics to obtain the user's preferences, and then dig out the resources that the user is interested in from a large amount of information. Recommendation algorithm is the core of recommender system design. Most of the traditional recommendation algorithms focus on the pursuit of the ac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06N3/00
CPCG06N3/006G06Q30/0631
Inventor 葛宏伟张乃强孙亮
Owner DALIAN UNIV OF TECH
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