Collaborative filtering method based on local optimization dimension reduction and clustering

A local optimization and collaborative filtering technology, which is applied in computer parts, character and pattern recognition, special data processing applications, etc., can solve the problem of inability to balance recommendation time and recommendation accuracy

Inactive Publication Date: 2020-02-07
HARBIN UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a method with good scalability and real-time and accurate recommendation to users in order to solve the problem that the relationship between recommendation time and recommendation accuracy cannot be weighed;

Method used

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  • Collaborative filtering method based on local optimization dimension reduction and clustering
  • Collaborative filtering method based on local optimization dimension reduction and clustering
  • Collaborative filtering method based on local optimization dimension reduction and clustering

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Experimental program
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Embodiment 1

[0059] A collaborative filtering method based on local optimization dimensionality reduction and clustering is characterized in that: first, the sparse user-item rating matrix is ​​subjected to dimensionality reduction processing to obtain a user feature matrix; secondly, clustering techniques are applied to the user feature matrix to obtain clusters of similar users ; Then predict the score of the target user on the user test set; finally select the N items with the highest score according to the prediction results to generate recommendations;

[0060] The collaborative filtering method based on local optimization dimensionality reduction and clustering is characterized in that: constructing the approximate difference matrix of the user-item rating matrix includes the following steps:

[0061] Step 1: The singular value decomposition theorem for local optimization shows that for all matrices C[k,n] where k rows represent users and n columns represent items C can be decomposed ...

Embodiment 2

[0069] The feature of the collaborative filtering method based on local optimization dimensionality reduction and clustering is: based on the local optimization dimensionality reduction method, the learning rate sr 1 When the difference between the mean square error (Mean Square Error, MSE) of the two iterations before and after the iteration is less than the threshold β, use a smaller learning rate sr 2 The iterative local optimization singular value decomposition method includes the following steps:

[0070] Step 1: Initialize

[0071] PMSE=0; Sum=0; sr 1 = 0.003; sr 2 = 0.00005; λ = 0.12; β = 0.0003

[0072] Step 2: For the user item set (i, j) in the training set D:

[0073] (1) Calculate user i's rating on item j:

[0074] (2) Calculate the error between the predicted score and the real score: Sum=r ij r ij ;

[0075] (3) For all features f (1≤f≤s) use the gradient descent method to solve:

[0076] x if =X if -sr 1 (r ij ·X if +λY jf ); Y jf =Y jf -sr...

Embodiment 3

[0089] The described collaborative filtering method based on local optimization dimensionality reduction and clustering is characterized in that: the K-means clustering method comprises the following steps:

[0090] Step 1: randomly select K users as K centroids;

[0091] Step 2: The remaining users are assigned to the nearest cluster according to their distance to each centroid; Pearson similarity is used to calculate the distance value; the similarity sim(i,j) between user i and user j is:

[0092]

[0093] where I ij The set of items rated jointly by user i and user j is C ip Indicates the rating of user i on item p with Respectively represent the average ratings of users i and j on the common rating items;

[0094] Step 3: Calculate the mean of the user class to define a new centroid;

[0095] Step 4: Recalculate the distance for each user to update the cluster to which the user belongs;

[0096] Step 5: Reassign according to the distance between the user and th...

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Abstract

Disclosed is a collaborative filtering method based on local optimization dimension reduction and clustering. An online user or a client often faces a problem recommendation system of key informationoverload, so that information overload can be effectively relieved, and the user is individually guided to find an attractor or an object meeting requirement in many possible projects. A recommendation system fully improves customer satisfaction by effectively mapping customer requirements and optimal products. At present, most recommendation methods cannot balance the relationship between recommendation time and recommendation accuracy. The method comprises the following steps: firstly, performing dimension reduction processing on a sparse user-project scoring matrix to obtain a user featurematrix; secondly, applying a clustering technology to the user feature matrix to obtain clusters of similar users; predicting the score of the target user on the user test set; and finally, selectingN items with the highest scores according to a prediction result to generate recommendations. The invention is used for accurately recommending the user in real time.

Description

technical field [0001] The invention relates to a collaborative filtering method based on local optimization dimension reduction and clustering. Background technique [0002] For most companies, it is becoming more and more important to understand the needs and preferences of online users or customers; however, online users or customers often face the problem of critical information overload; recommendation systems can effectively alleviate information overload and guide users in a personalized way Find attractive or satisfying objects among many possible items; recommender systems can fully improve customer satisfaction by effectively mapping customer needs with optimal products; however, the quality of recommender systems mainly depends on the selected recommendation method ;Traditional recommendation methods are mainly divided into three categories: content-based recommendation methods, collaborative filtering recommendation methods and hybrid recommendation methods; [...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06K9/62G06F17/16
CPCG06F16/9536G06F17/16G06F18/21322G06F18/23213
Inventor 尹芳宋垚孟迪
Owner HARBIN UNIV OF SCI & TECH
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