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Method for screening nearest neighbor by using potential neighbor relation graph in recommendation system

A neighbor relationship and nearest neighbor technology, applied in the field of recommendation, can solve the problems of close and complex data internal relationship, unbalanced value density distribution, low operation efficiency, etc., to improve recommendation efficiency, improve recommendation accuracy, and reduce scale. Effect

Active Publication Date: 2017-01-25
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The more pairwise comparisons, the lower the efficiency of the operation, but the higher the probability of finding the nearest neighbor; on the contrary, the fewer the number of pairwise comparisons, the higher the operating efficiency, but the higher the possibility of missing the nearest neighbor. High, thus affecting the recommended accuracy
In addition, the internal correlation of data in the recommendation system is close and complex, and the distribution of value density is extremely uneven. The calculation of massive data in the recommendation system cannot rely on the statistical analysis and iterative calculation of global data like the small sample data set, and needs to explore the data. On-demand reduction method

Method used

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  • Method for screening nearest neighbor by using potential neighbor relation graph in recommendation system
  • Method for screening nearest neighbor by using potential neighbor relation graph in recommendation system
  • Method for screening nearest neighbor by using potential neighbor relation graph in recommendation system

Examples

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Embodiment 1

[0029] In a movie recommendation system, given user set U, item set I, each item is a movie; F i Represents the feature vector of item i, which is used to describe the movie genre, award-winning type, actor and director, etc.; R i Denotes the utility vector of item i ∈ I, r u,i ∈R i is the rating of item i by user u ∈ U. The recommendation system uses the potential neighbor relation graph to screen the nearest neighbors of the target item i, and then uses the ratings of the target user u on all the nearest neighbors of item i to predict the rating of user u on item i. In the present embodiment 1, the object set is an item set, and the specific process of screening the nearest neighbors of the items is as follows:

[0030] (1) Generate a cluster set. Use the fuzzy clustering technique to assign the item i∈I to multiple clusters with a certain probability according to the item characteristics, thereby generating the item cluster set C.

[0031] (2) Construct the potential n...

Embodiment 2

[0038] In a movie recommendation system, given user set U, item set I, each item is a movie; F q Represents the feature vector of user q, which is used to describe the user's gender, age, occupation, etc.; R q Denotes the utility vector of user q ∈ U, r q,m ∈R u is the rating of user q on item m ∈ I. The recommendation system uses the potential neighbor relation graph to screen the nearest neighbors of the target user q, and then uses the ratings of all the nearest neighbors of the target user q to the item m to predict the rating of the user q on the item m. In Embodiment 2, the object set is a user set, and the specific process of screening the nearest neighbors of the users is as follows:

[0039] (1) Generate a cluster set. Use fuzzy clustering technology to assign user u∈U to multiple clusters with a certain probability according to user characteristics, thereby generating user cluster set C.

[0040] (2) Construct the potential neighbor relationship graph G correspo...

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Abstract

The invention discloses a method for screening a nearest neighbor by using a potential neighbor relation graph in a recommendation system. The method comprises the following steps: (1) generating an object cluster set C with a redundant feature; (2) constructing the potential neighbor relation graph corresponding to the cluster set C; (3) quantifying weight of each edge in the potential neighbor relation graph, wherein the weight of the edge represents the possibility that two adjacent objects of the edge become the nearest neighbors; (4) clipping a potential neighbor relation to weed out redundant comparisons; (5) screening the nearest neighbor of a target according to the clipped potential neighbor relation graph. According to the screening method, on the premise of guaranteeing the recommendation precision, a recommendation based on a complete large-scale data set is mapped to a recommendation of a data set with a relatively small scale, so that the scale of the recommendation system is reduced, and high efficiency of the recommendation method is guaranteed.

Description

technical field [0001] The invention relates to the technical field of recommendation, in particular to a method for screening nearest neighbors using a potential neighbor relationship graph in a recommendation system. Background technique [0002] Currently, the ever-increasing amount of data makes it take a lot of time for users to find valuable information. Collaborative filtering is considered to be one of the effective technologies to solve the problem of information overload, and has been widely used in recommendation fields such as movies, music, books, travel, and news. The main idea of ​​the collaborative filtering method is to predict the target user's view on the item based on similar users' preferences on the item, or to recommend the target item based on the user's opinion on the similar item. Therefore, a key problem that collaborative filtering needs to solve is: how to effectively select the nearest neighbors of target users or items. However, the recommend...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/2462G06F16/285G06F16/35G06F16/635G06F16/735G06F16/9535
Inventor 王晓军
Owner NANJING UNIV OF POSTS & TELECOMM
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