Cluster-based increment digital book recommendation method

A technology for digital books and recommendation methods, which is applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of poor real-time performance and low recommendation efficiency, and achieves the effects of time and space efficiency and method efficiency.

Active Publication Date: 2014-06-25
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the shortcomings of low efficiency and poor real-time performance of traditional book recommendation in digital libraries, and provide a high-quality, novel incremental digital book recommendation method based on clustering

Method used

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  • Cluster-based increment digital book recommendation method
  • Cluster-based increment digital book recommendation method
  • Cluster-based increment digital book recommendation method

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Embodiment

[0054] figure 1 It shows the overall structure diagram of a clustering-based incremental digital book recommendation method of the present invention, which is divided into two parts: the first part incrementally uses user ratings to update user representations, and uses user representations as method inputs to increase Quantitative clustering, the second part is to use the ranking function to generate recommendation results based on the clustering results.

[0055] The clustering method used in the first part is as follows:

[0056]

[0057] The input of the clustering algorithm in the first part is the user representation vector generated by mining the user’s reading book information; the output is the result of clustering; for the input user representation vector, first check that the user representation vector is a new user that has never been clustered , or the old users who have been clustered but have new scoring information and need to be re-clustered; if it is the...

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Abstract

The invention discloses a cluster-based increment digital book recommendation method. The method includes the steps of firstly, obtaining information of books read by a user from a website access log of a user, and then generating a user representation vector; secondly, selecting a cluster to be calculated through a dimensionality array, and then calculating the cosine similarity between the user and the cluster to form a candidate set; thirdly, finding a cluster most similar to the target user from the candidate set, and then conducting clustering according to the combination result and renewing the cluster center and the cluster diameter in an increment mode; fourthly, ranking items in the cluster with the cluster center value serving as a ranking function, and enabling an item with the high ranking position to serve as the recommendation result. According to the method, the favor information of the user for books can be excavated from the book access log of the user, then, recommendation is conduced for the user, extensibility and instantaneity of the recommendation method are improved, the utilization rate of digital book resources is increased, and reading experience of the user is enhanced.

Description

technical field [0001] The invention relates to the fields of recommendation system, incremental learning, digital library and the like, in particular to a method for recommending incremental digital books based on clustering. Background technique [0002] There are a large number of digital book resources in the digital library, how to make readers use these rich and valuable digital book resources and have a better experience is very important. The traditional retrieval-based information acquisition technology can no longer fully meet people's needs, and personalized recommendation is gradually becoming an indispensable part of digital libraries. [0003] Traditional recommendation methods are effective and easy to explain, but consume storage space and computation time due to the need to load and view the entire dataset during the recommendation process. As the size of the data increases, this approach becomes inefficient and very limited by available resources. The inc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/337G06F16/9535
Inventor 张寅王宇奇伊灯庄越挺魏宝刚
Owner ZHEJIANG UNIV
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