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Tensor decomposition based context-dependent position recommendation method

A technology of tensor decomposition and recommendation method, which is applied in the field of location push service and personalized recommendation technology, can solve problems such as poor performance and inability to solve effectively, so as to improve performance, improve response speed, and reduce calculation and communication load Effect

Inactive Publication Date: 2016-07-27
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

[0005] In the above methods, since the impact of time context on location recommendation for users is not considered, the performance is very poor in data sparse scenarios, and the cold start problem of new users and new projects cannot be effectively solved.

Method used

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  • Tensor decomposition based context-dependent position recommendation method
  • Tensor decomposition based context-dependent position recommendation method
  • Tensor decomposition based context-dependent position recommendation method

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Experimental program
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Embodiment Construction

[0022] specific implementation plan

[0023] The core idea of ​​the present invention is to propose a context-dependent location recommendation method based on tensor decomposition to provide high-performance location recommendation results.

[0024] refer to figure 1 , the present invention realizes steps as follows:

[0025] Step 1. Construct a three-dimensional check-in tensor A.

[0026] According to user i, location j, time t and user i's score A for location j at time t in all check-in data of the city to be recommended ijk Record and construct a user-place-time three-dimensional check-in tensor A, where A ijk is the element corresponding to row i, column j, and degree t of tensor A.

[0027] Step 2. According to the user records in the check-in data, the user similarity matrix B is calculated by using the weighted Pearson similarity.

[0028] (2a) Calculate the weighting coefficient w according to the following formula uv :

[0029] w ...

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Abstract

The invention discloses a tensor decomposition based context-dependent place recommendation method and mainly solves the problem of poor place recommendation quality in the prior art. The method is implemented by the steps of 1, constructing a three-dimensional sign-in tensor A and a user similarity matrix B by utilizing sign-in data of all users of a to-be-recommended city; 2, obtaining a three-dimensional tensor A by using a high-order singular value decomposition algorithm; 3, obtaining the current position of a to-be-recommended user c; and 4, according to the three-dimensional tensor A, performing place recommendation on the to-be-recommended user c. According to the method, the communication traffic between the user and a recommendation system is reduced by utilizing tensor decomposition, and the effectiveness and reliability of a place recommendation result in a data sparsity scene is ensured in combination with a time context and historical user data; and the method can be applied to position based place recommendation services in a social network.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a personalized recommendation technology related to time context, which can be used for a location push service in a location-based social network. Background technique: [0002] With the development of information technology and mobile terminal technology, the amount of information on the Internet is growing exponentially. Faced with the surge of data, people hope to dig out many important information hidden behind, so that these data can be better used to serve people. Service providers have a large amount of personal information and historical records related to users. Using these data to actively recommend relevant services to users can help users make choices, discover valuable items and offline services they are interested in, and improve User experience; on the other hand, let items and services be displayed to users who are interested in them through ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/9537
Inventor 朱晓妍郝日佩池浩田裴庆祺
Owner XIDIAN UNIV
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