Method and device for acquiring recommended object
A technology of objects and new objects, applied in the computer field, can solve problems such as the inability to measure the similarity between vectors, the inability to directly apply search, and the inner product not satisfying the triangle inequality, etc., to achieve fast search speed, wide application range, and high calculation efficiency effect
Active Publication Date: 2015-09-23
BEIJING JINGDONG SHANGKE INFORMATION TECH CO LTD +1
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However, since the inner product does not satisfy the triangular inequality and cannot measure the similarity between vectors, the commonly used clustering, high-dimensional index tree, local awareness hashing and other schemes for building high-dimensional indexes cannot be directly applied to the inner product space. search for
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[0021] Exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present invention to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
[0022] figure 1 is a schematic diagram of main steps of the method for acquiring recommended objects according to an embodiment of the present invention. Such as figure 1 As shown, the method mainly includes the following steps S1 to S5.
[0023] Step S1: Construct the original user matrix and the original object matrix. Wherein, each row in the original user matrix is...
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The invention provides a method and device for acquiring a recommended object, having the advantages of rapid searching speed, wide application range and so on. The method comprises the steps of building an original user array and an original object array, wherein each line in the original user array is an original user vector, each original user vector represents multiple attributive characters of one user, each line in the original object array is an original object vector, and each original object vector represents multiple attributive characters of one object; performing order preserving transformation on the original user array and original object array, so as to obtain a new user array and a new object array; building an object high dimension index structure of new object vectors in the new object array; performing nearest neighbor searching on each new user vector in the new user array in the object high dimension index structure so as to obtain a nearest neighbor new object vector corresponding to the new user vector; and outputting a mapping of the user and recommended object according to all the new user vectors and the nearest neighbor new object vectors corresponding to the new user vectors.
Description
technical field [0001] The present invention relates to the field of computer technology, in particular to a method and device for acquiring recommended objects. Background technique [0002] With the development of Internet technology, people have entered the era of information overload. When a user visits a website, a large number of objects such as news, merchandise, video, music, etc. are presented in front of the user. How to filter out the content that users are really interested in is of great significance to improving the traffic and income of the website. Recommender system-related technologies have been used more and more widely in this context. [0003] In recent years, the matrix factorization algorithm has become the mainstream in the field of recommendation system research. Studies have shown that using matrix factorization technology in personalized recommendation can get better results than traditional neighborhood-based collaborative filtering. The matrix...
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IPC IPC(8): G06F17/30
CPCG06F16/951
Inventor 杨德坤郑宸贺炜王彦明
Owner BEIJING JINGDONG SHANGKE INFORMATION TECH CO LTD