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Similarity measuring method improved through collaborative filtering recommendation algorithm

A collaborative filtering recommendation and similarity measurement technology, applied in computing, special data processing applications, instruments, etc.

Inactive Publication Date: 2014-01-08
SUZHOU UNIV
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  • Application Information

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Problems solved by technology

Therefore, in the case of extremely sparse user rating data, the measurement method of related similarity also has certain drawbacks.

Method used

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  • Similarity measuring method improved through collaborative filtering recommendation algorithm
  • Similarity measuring method improved through collaborative filtering recommendation algorithm
  • Similarity measuring method improved through collaborative filtering recommendation algorithm

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Embodiment Construction

[0027] Hereinafter, the present invention will be described in detail with reference to the drawings and examples. It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0028] figure 1 It is a flow chart of the improved similarity measurement method in the collaborative filtering recommendation algorithm provided by the preferred embodiment of the present invention. Such as figure 1 As shown, the improved similarity measurement method in the collaborative filtering recommendation algorithm provided by the preferred embodiment of the present invention includes steps S1-S3.

[0029] Step S1: Create user set U={U 1 ,U 2 ,...,U n}In n user-to-item sets I={I 1 ,I 2 ,...,I m} in the scoring matrix R(n×m) of m items, with R a,i Indicates user U a For item I i rating, where U a ∈U,I i ∈I.

[0030] specifically, figure 2 It is a schematic diagram of user-item...

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Abstract

A similarity measuring method improved through a collaborative filtering recommendation algorithm includes the following steps of (S1) building a rating matrix R(n*m) of n users in a user set U={U1, U2,..., Un} to m items in an item set I={I1, I2,..., Im}, taking Ra,i as representation of rating of an item Ii, wherein Ua belongs to U and Ii belongs to I, (S2) calculating the similarity sim(Ua, Ub) between a user Ua and a user Ub and the similarity sim(Ii, Ij) between an item Ii and an item Ij, defining a similarity influence divisor epsilon, so that sim'(Ua, Ub) equals to epsilon* sim(Ua, Ub) and sim'(Ii, Ij) equals to epsilon* sim'(Ii, Ij), (S3) taking a parameter lambada in an interval between 0 and 1, and predicting rating of the users to the items according to lambada, epsilon, an average rating value of the users to the items, similarity between the users and similarity between the items.

Description

technical field [0001] The invention relates to a collaborative filtering (Collaborative filtering) recommendation technology in the research of a recommendation system, in particular to an improved similarity measurement method in a collaborative filtering recommendation algorithm. Background technique [0002] With the rapid popularization of the Internet and the rapid development of e-commerce, the information data on the Internet has increased rapidly, and how to enable users to quickly and efficiently obtain the required information from the vast ocean of data has become more and more urgent. Therefore, providing active recommendation services for users is increasingly being applied to various portal websites and e-commerce systems. These systems provide recommendation services by collecting users' historical information, learning users' interests and behavior patterns, and analyzing users' behavioral characteristics. [0003] Collaborative filtering recommendation tec...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9535
Inventor 赵朋朋吴健冒九妹鲜学丰崔志明
Owner SUZHOU UNIV
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