Method and system for solving cold start problem in collaborative filtering technology

A collaborative filtering recommendation and cold start technology, applied in genetic rules, relational databases, structured data retrieval, etc., can solve problems affecting recommendation quality, affecting final clustering division, low efficiency and local optimum

Active Publication Date: 2017-11-24
INNER MONGOLIA UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, the traditional K-Means clustering algorithm is random in the selection of the initial user center point, which is not only inefficient but also produces a local optimal situation, which affects the final clustering division and thus affects the entire recommendation quality
The selection crossover probability and mutation probability in the traditional genetic algorithm are fixed, that is, they remain unch

Method used

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  • Method and system for solving cold start problem in collaborative filtering technology

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Embodiment 1

[0116] like figure 1 As shown, a method for solving the cold start problem in the collaborative filtering recommendation technology of the present invention comprises the following steps:

[0117] Step S11: selecting a data set, the data set includes user-item rating data and user or item attribute information.

[0118] Step S12: Based on the user-item rating data, an initial user or item clustering model is established through an optimized genetic algorithm.

[0119] Step S13: According to the initial user or item clustering model, perform K-Means clustering on the user-item rating data to obtain the user or item clustering model.

[0120] Step S14: According to the attribute information of the new user or new item and the attribute information of the user or item, calculate the entropy value of the new user or new item divided into various clusters of the user or item clustering model, and classify the new user or item according to the obtained entropy value New items are ...

Embodiment 2

[0123] like Figure 2-4 As shown, another method of the present invention to solve the cold start problem in the collaborative filtering recommendation technology includes the following steps:

[0124] Step S21: Select a data set. As a possible implementation mode, select the Movielens data set, including user-item rating data of 6,040 users with more than 1,000,000 items for 3,900 movies, and user attribute information of 6,040 users. The user-item rating The data includes user id, item id and rating data, and the user attribute information includes user id, user gender and user age.

[0125] Step S22: Optimizing the genetic algorithm, including:

[0126] Step a. Use the weighted silhouette coefficient as the fitness function of the individual in the population:

[0127]

[0128]

[0129] f=S (3)

[0130]

[0131]

[0132] Among them, a(i) is the average distance from sample i to other samples in the same cluster, b(i) represents the minimum value of the averag...

Embodiment 3

[0207] like Figure 5 As shown, a system for solving the cold start problem in the collaborative filtering recommendation technology of the present invention includes:

[0208] The selection module 51 is used to select a data set, the data set includes user-item rating data and user or item attribute information.

[0209] The initial model building module 52 is configured to establish an initial user or item clustering model through an optimized genetic algorithm based on the user-item rating data.

[0210] The clustering module 53 is configured to perform K-Means clustering on the user-item rating data according to the initial user or item clustering model to obtain a user or item clustering model.

[0211] The clustering module 54 is used to calculate the entropy value of the new user or new item divided into various clusters of the user or item clustering model according to the attribute information of the new user or new item and the user or item attribute information, an...

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Abstract

The invention belongs to the technical field of personalized recommendation, and particularly relates to a method and system for solving a cold start problem in a collaborative filtering technology. The method for solving the cold start problem in the collaborative filtering technology comprises the steps that a data set is selected; an initial user or project clustering model is built through an optimized genetic algorithm; clustering is conducted on the basis of the initial user or project clustering model, and a user or project clustering model is obtained; entropy values of new users or new projects to all kinds of clusters in the clustering model are calculated, and the new users or the new projects are subjected to class cluster dividing; the new users or the new projects are recommended. The invention further provides a system for solving the cold start problem in the collaborative filtering technology. The system comprises a selection module, an initial model building module, a clustering module, a class cluster dividing module and a recommendation generation module. Accordingly, an improved genetic algorithm is utilized for conducting K-Means clustering, the initial user or project clustering model is generated, and recommendation is generated for the new users or the new projects.

Description

technical field [0001] The invention belongs to the technical field of personalized recommendation, and in particular relates to a method and system for solving the cold start problem in collaborative filtering recommendation technology. Background technique [0002] Information overload is one of the most serious problems in the big data environment, and recommender systems, as an effective way to alleviate this problem, have attracted more and more attention from industry and academia. As one of the most widely used recommendation technologies, collaborative filtering recommendation technology has achieved a lot of achievements in both theoretical and applied research, but there is a cold start problem, which affects the recommendation quality of collaborative filtering recommendation technology. [0003] The cold start problem includes the new user cold start problem and the new project cold start problem. The traditional collaborative filtering recommendation technology...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06N3/12
CPCG06F16/285G06N3/126
Inventor 田保军胡培培杜晓娟杨浒昀
Owner INNER MONGOLIA UNIV OF TECH
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