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The Differentiation of Residents’ Cultural Consumption Tendency and Consumption Recommendation System Based on Network Inference Algorithm

100%

Lian N. , Xu H. , Zhang F.

tom Vol. 49, No. 2

121--138

To address the issue of insufficient accuracy in consumer recommendation systems, a new biased network inference algorithm is proposed based on traditional network inference algorithms. This new network inference algorithm can significantly improve the resource allocation ability of the original one, thereby improving recommendation performance. Then, the performance of this algorithm is verified through comparative experiments with network-based inference algorithms, network inference algorithms with initial resource optimization, and heterogeneous network inference algorithms. The results showed that the accuracy of the new network inference algorithm was 24.5%, which was superior to traditional one. In terms of system performance testing, the recommendation hit rate of the new network inference algorithm increased by 13.97%, which was superior to the other three comparative algorithms. The experimental results indicated that a novel network inference algorithm with bias can improve the performance of consumer recommendation systems, providing new ideas for improving the performance of consumer recommendation systems.

Recommendation systems for nitrogen - a review

75%

MacKenzie G. H. , Taureau J.-C.

Nawozy i Nawożenie

2001

tom 03

nr 4[09]

The Polish fertilization recommendation system NawSald

75%

Jadczyszyn T.

2009

nr 37

Systemy rekomendacji do zastosowania w przemyśle chemicznym

63%

Albiniak M. , Badura X. , Kwiecień A.

tom T. 98, nr 11

1690--1692

Development of fertilizer recommendation algorithms

63%

Fotyma M. , Fotyma E.

nr 1[06]

Fertilizer recommendation schemes officially used in Czech Republic, Latvia, Poland, Slovak Republic and United Kingdom

63%

Bujnovsky R. , Fotyma M.

nr 1[06]

A Comparison between a relational and a graph database in the context of a recommendation system

51%

Stanescu L.

2021

tom Vol. 26

133-139

This paper presents a comparison between relational and graph database systems' performance in a web application recommendation system. The comparison is conducted on five different queries starting with simple ones, leading up to more complex queries, that are performed in a typical web social application. The implementation is done in C# using .NET framework and the database systems used are SQL Server and Neo4J. To effectively test the performance of both graph and relational database systems, tests were performed on 4 data sets. The tests imply performing 5 different retrieval queries taken in order of difficulty both in SQL and Neo4J.

Soil fertility evaluation in Czech Republic, Latvia, Poland, Slovak Republic and the United Kingdom

51%

Bujnovsky R. , Cermak P. , Machacek V. , Fotyma M. , Igras J. , Jadczyszyn T. , Karklins A. , Shepherd M. , Bhogal A.

nr 2[03]