Personalized position recommendation method based on ensemble learning

Abstract

The invention discloses a personalized position recommendation method based on ensemble learning, and the method comprises the steps: 1, converting a sign-in data set into a scoring matrix; 2, selecting a plurality of recommendation sub-algorithms, dividing the addresses accessed by the active users into a training sub-data set and an evaluation sub-data set, and calculating a pre-score for an address in the evaluation data set and an unaccessed address by utilizing the training sub-data set and each sub-algorithm; 3, calculating recommendation precision F1 of each sub-model by utilizing the evaluation data set, and generating a precision weight value set; 4, selecting an information gain IG as a stability index, evaluating the stability of each sub-model, and calculating a stability weight value set; 5, calculating a final total weighting coefficient for the active users, wherein the integration model fuses the pre-scores of the sub-algorithms for the unaccessed addresses according toa total weighting coefficient to generate a final prediction score; 6, evaluating the comprehensive performance of the method provided by the invention and each sub-algorithm before integration, andevaluating the effectiveness of the method.

Description

technical field [0001] The invention relates to a personalized location recommendation method based on integrated learning in a social network, and belongs to the technical field of artificial intelligence and machine learning. Background technique [0002] Location-based Social Networks (LBSNs) is the product of the gradual integration and development of Online Social Networks and Location-based Services. The world provides a platform for close connection. In recent years, with the widespread popularization of mobile devices and the rapid development of positioning technology, a large number of location-based social networks have emerged rapidly. In LBSNs, complex social relationships can be established between users, such as friend relationships, colleague relationships, relative relationships, etc. Users can also use the geographic information added in social networks to view some points of interest (points-of-interest, POIs), such as restaurants, shops, movie theaters,...

AI Technical Summary

Problems solved by technology

Neglecting any of them will affect the stickiness of users and reduce the profit of service providers

[0012] (4) At present, the few stability studies almost limit the application scenarios to malicious attacks, such as an attacker trying to recommend pre-set items to users

[0013] The above is the deficiency of the existing recommendation system technology based on ensemble learning, which brings great disadvantages in the design, development, deployment and operation of different e-commerce platforms, especially in the network platform of massive project information. The decline in quality, which in turn affects the sales performance of the e-commerce system

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