Wyniki wyszukiwania - BazTech

1

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

Lian Naiyu, Xu Hengzhe, Zhang Feiyang

Foundations of Computing and Decision Sciences

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2024

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Vol. 49, No. 2

121--138

EN

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.

2

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

Stanescu Liana

Annals of Computer Science and Information Systems

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2021

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Vol. 26

133-139

EN

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.

3

Systemy rekomendacji do zastosowania w przemyśle chemicznym

Albiniak Marcin, Badura Xymena, Kwiecień Andrzej

Przemysł Chemiczny

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2019

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T. 98, nr 11

1690--1692

4

Recommendation systems with the quantum k-NN and Grover algorithms for data processing

Sawerwain Marek, Wróblewski Marek

International Journal of Applied Mathematics and Computer Science

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2019

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Vol. 29, no. 1

139--150

EN

In this article, we discuss the implementation of a quantum recommendation system that uses a quantum variant of the k-nearest neighbours algorithm and the Grover algorithm to search for a specific element in an unstructured database. In addition to the presentation of the recommendation system as an algorithm, the article also shows the main steps in construction of a suitable quantum circuit for realisation of a given recommendation system. The computational complexity of individual calculation steps in the recommendation system is also indicated. The verification of the correctness of the proposed system is analysed as well, indicating an algebraic equation describing the probability of success of the recommendation. The article also shows numerical examples presenting the behaviour of the recommendation system for two selected cases.

5

Using contextual conditional preferences for recommendation tasks: a case study in the movie domain

Karpus A., Di Noia T., Tomeo P., Goczyła K.

Studia Informatica

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2016

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Vol. 37, nr 1

7--18

EN

Recommendation engines aim to propose users items they are interested in by looking at the user interaction with a system. However, individual interests may be drastically influenced by the context in which decisions are taken. We present an attempt to model user interests via a set of contextual conditional preferences. We show that usage of proposed preferences gives reasonable values of the accuracy and the precision even when the dataset is quite small.

PL

Systemy rekomendacyjne sugerują użytkownikom produkty, którymi mogą być zainteresowani, na podstawie wcześniejszej interakcji z systemem. Jednak duży wpływ na decyzję użytkownika ma kontekst, w którym jest ona podejmowana. W artykule zaproponowano model zainteresowań użytkownika jako zbiór kontekstowych preferencji warunkowych i pokazano, że z ich wykorzystaniem można uzyskać dużą dokładność i precyzję rekomendacji, nawet dla małych zbiorów danych.

6

Service Recommendation on Wiki-WS Platform

Sobecki A.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2015

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Vol. 19, No 4

445--453

EN

The article presents the issues of recommendation services for users. The commonly used solutions shown include known classes of recommendation systems and information about their area of use and the most frequently used algorithms. The issue of choosing the services has been described based on the Wiki-WS platform including a model of cooperation with the recommendation system. The conclusion from the analysis of the proposed model and available algorithms is that there is a need to create a hybrid service recommendation algorithm in order to fulfill the requirements of those using a service platform like Wiki-WS.