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Personalized recommendation method and system based on context awareness and feature interaction modeling

A technology of feature interaction and recommendation method, which is applied in sales/lease transactions, special data processing applications, forecasting, etc., and can solve problems such as no context-user/item interaction modeling, inappropriateness, and inability to directly reflect the influence of context information

Inactive Publication Date: 2020-06-09
QILU UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] (7) Using context in a recurrent neural network (RNN) to achieve an efficient recommendation process, the author conducts an empirical analysis of the classic feature ensemble method and proves that this method is not suitable for capturing the most important feature intersections;
[0011] It is worth noting that in all the methods mentioned above, there are still more or less some shortcomings, or there is no context-user / item interaction modeling, and it is impossible to directly reflect the impact of context information on users / items , or no measures are taken to distinguish the influence of different contextual information on users / items

Method used

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  • Personalized recommendation method and system based on context awareness and feature interaction modeling
  • Personalized recommendation method and system based on context awareness and feature interaction modeling
  • Personalized recommendation method and system based on context awareness and feature interaction modeling

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Experimental program
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Embodiment 1

[0079] as attached figure 1 As shown, the personalized recommendation method based on context awareness and feature interaction modeling of the present invention is to construct a feature interaction network model (FINM) based on context awareness feature interaction, specifically as follows:

[0080] S1. Construct a context feature information attribute model. According to different data sets, select the specific context environment where the user is in, and construct a context feature information vector according to the context information; where the context feature information of user information is different in different data sets, usually It is considered that data such as time data information and geographic data information will be the most frequently occurring contextual feature information, but in the application field Food data set of the present invention, virtual sensibility and hunger are selected as unique contextual feature information; details are as follows:

...

Embodiment 2

[0118] The data set (Food) adopted in the present invention is the real data of collection, and this data set is provided by the people such as Ono, has included 212 users' 6360 evaluation information on 20 kinds of food, according to reasonable inference, We believe that each rating record of a user is related to the two contextual information of virtuality and hunger. The first is the context information factor Virtuality (Virtuality) describes whether the user's evaluation is virtual or real (the context information has two context values: real and virtual), and the second context information factor is hunger (Hunger ), the context information describes the hunger level of the user when evaluating (there are three context values: hungry, normal and full), as shown in the following table:

[0119] data set User number Number of items number of contextual factors Number of Interaction Records scoring scale food 212 20 2 6360 1-5 points

[0...

Embodiment 3

[0151] as attached figure 2 As shown, the personalized recommendation system based on context perception and feature interaction modeling of the present invention includes,

[0152] The context feature information selection module is used to construct the context feature information attribute model, select the specific context of the user / item according to different data sets, and construct the context feature information vector according to the context information;

[0153] The context feature information-user / context feature information-item interaction module is used to construct the context feature information-user / context feature information-item interaction model, using bilinear function to combine different context feature information vectors and user / item potential feature vectors Mapped to the shared latent space, and then the interaction result is obtained through the output function, so as to capture the interaction between the context and the user / item and obtain ...

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Abstract

The invention discloses a personalized recommendation method and system based on context awareness and feature interaction modeling. The invention belongs to the technical field of data mining, in order to solve the technical problem of how to recommend for a user according to preferences of the user in different context environments and improve the accuracy of recommendation, the adopted technical scheme is as follows: a feature interaction network model based on context-aware feature interaction is constructed, and the method specifically comprises the following steps: constructing a contextfeature information attribute model; constructing a context feature information-user / context feature information-article interaction model; constructing an influence degree model of different contextfeature information on the user / article; constructing an overall influence model of the context environment on the potential feature information of the user / article; and constructing a feature interaction network prediction model. The invention further discloses a personalized recommendation system based on context awareness and feature interaction modeling.

Description

technical field [0001] The invention belongs to the technical field of data mining and is applied to personalized recommendation of networks, in particular to a personalized recommendation method and system based on context perception and feature interaction modeling. Background technique [0002] In recent years, with the emergence of emerging technologies such as online ordering, the catering industry has ushered in new developments. However, in the context of a variety of food, users often find it difficult to choose accurate food that suits their tastes. Therefore, for the current large For most food ordering software and physical restaurants, how to recommend satisfactory food to users according to their actual needs is the top priority. At present, common food recommendation systems often only pay attention to the characteristics of users and items themselves, and do not consider the special context of users and items, so it is impossible to observe the changes of user...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06Q10/04G06Q30/06G06Q50/12
CPCG06F16/9535G06Q30/0631G06Q50/12G06Q10/04
Inventor 高茜马鹏程
Owner QILU UNIV OF TECH
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