scholarly journals CMBF: Cross-Modal-Based Fusion Recommendation Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5275
Author(s):  
Xi Chen ◽  
Yangsiyi Lu ◽  
Yuehai Wang ◽  
Jianyi Yang
Keyword(s):  
Recommendation System ◽  
Fusion Method ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Actual Interaction ◽  
The Cross ◽  
Ablation Study ◽  
Available Information ◽  
The Relationship ◽  
Single Modality

A recommendation system is often used to recommend items that may be of interest to users. One of the main challenges is that the scarcity of actual interaction data between users and items restricts the performance of recommendation systems. To solve this problem, multi-modal technologies have been used for expanding available information. However, the existing multi-modal recommendation algorithms all extract the feature of single modality and simply splice the features of different modalities to predict the recommendation results. This fusion method can not completely mine the relevance of multi-modal features and lose the relationship between different modalities, which affects the prediction results. In this paper, we propose a Cross-Modal-Based Fusion Recommendation Algorithm (CMBF) that can capture both the single-modal features and the cross-modal features. Our algorithm uses a novel cross-modal fusion method to fuse the multi-modal features completely and learn the cross information between different modalities. We evaluate our algorithm on two datasets, MovieLens and Amazon. Experiments show that our method has achieved the best performance compared to other recommendation algorithms. We also design ablation study to prove that our cross-modal fusion method improves the prediction results.

scholarly journals SIS dynamic network model based on product semantics from the perspective of infectious disease dynamics

2021 ◽  
Vol 235 ◽  
pp. 03035
Author(s):  
jiaojiao Lv ◽  
yingsi Zhao
Keyword(s):  
Network Model ◽  
Purchase Intention ◽  
Recommendation System ◽  
Optimal Algorithm ◽  
Dynamic Network ◽  
Distribution Model ◽  
Recommendation Algorithm ◽  
Model Based ◽  
Dynamic Network Model ◽  
Recommendation Algorithms

Recommendation system is unable to achive the optimal algorithm, recommendation system precision problem into bottleneck. Based on the perspective of product marketing, paper takes the inherent attribute as the classification standard and focuses on the core problem of “matching of product classification and recommendation algorithm of users’ purchase demand”. Three hypotheses are proposed: (1) inherent attributes of the product directly affect user demand; (2) classified product is suitable for different recommendation algorithms; (3) recommendation algorithm integration can achieve personalized customization. Based on empirical research on the relationship between characteristics of recommendation information (independent variable) and purchase intention (dependent variable), it is concluded that predictability and difference of recommendation information are not fully perceived and stimulation is insufficient. Therefore, SIS dynamic network model based on the distribution model of SIS virus is constructed. It discusses the spreading path of recommendation information and “infection” situation of consumers to enhance accurate matching of recommendation system.

The Application of Hybrid Recommendation Algorithm in Education Cloud

2014 ◽  
Vol 551 ◽  
pp. 670-674 ◽  
Author(s):  
Gai Zhen Yang
Keyword(s):  
Cloud Computing ◽  
Recommendation System ◽  
Research Work ◽  
Operating Efficiency ◽  
Cloud Platform ◽  
Recommendation Algorithm ◽  
Development Platform ◽  
Recommendation Algorithms ◽  
Technology Applications ◽  
Cloud Development

When we face large amounts of data, how can we find the most suitable educational resources quickly has become a pressing issue. In this paper, on the basic of comparative study on traditional recommendation algorithms, we use the cloud computing to solve the traditional collaborative filtering algorithms suffer from scalability issues, the proposed algorithm is applied to the combination of recommended teaching cloud platform program, the program according to different recommended by demand different recommendation strategies; open source project Hadoop as a cloud development platform of the algorithm; recommendation algorithm, algorithm on top of Hadoop to achieve improved operating efficiency is relatively high, ideal parallel performance, fully proved the cloud platform and recommended algorithm combining the advantages. The research work on the recommendation system and teaching cloud computing technology applications to provide a useful reference.

scholarly journals Autonomous recommender system architecture for virtual learning environments

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Julián Monsalve-Pulido ◽  
Jose Aguilar ◽  
Edwin Montoya ◽  
Camilo Salazar
Keyword(s):  
Learning Styles ◽  
Learning Environments ◽  
Recommendation System ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Specific Recommendation ◽  
Digital Resources ◽  
Recommendation Algorithms ◽  
Available Information ◽  
Hybrid Recommendation

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

scholarly journals Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sheng Bin ◽  
Gengxin Sun
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Social Relationships ◽  
Matrix Factorization ◽  
Social Relationship ◽  
Recommendation System ◽  
Social Recommendation ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms

With the widespread use of social networks, social recommendation algorithms that add social relationships between users to recommender systems have been widely applied. Existing social recommendation algorithms only introduced one type of social relationship to the recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization recommendation algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization recommendation algorithm has a significant improvement over the traditional and matrix factorization recommendation algorithms that integrate a single social relationship.

scholarly journals Research on E-Commerce Platform-Based Personalized Recommendation Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Zhijun Zhang ◽  
Gongwen Xu ◽  
Pengfei Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Prediction Formula ◽  
Recommendation Algorithm ◽  
Real Dataset ◽  
Recommendation Algorithms ◽  
Filling Method ◽  
System Time ◽  
Different Levels

Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.

NCGAN:A neural adversarial collaborative filtering for recommender system

2021 ◽  
pp. 1-9
Author(s):  
Jinyang Sun ◽  
Baisong Liu ◽  
Hao Ren ◽  
Weiming Huang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Measurement Tool ◽  
Recommendation Algorithm ◽  
Generative Adversarial Network ◽  
Experimental Performance ◽  
Adversarial Network ◽  
Recommendation Algorithms ◽  
The Neural Networks ◽  
Model Training

The major challenge of recommendation system (RS) based on implict feedback is to accurately model users’ preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users’ preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filtering and generative adversarial network (GAN). We use the neural networks to extract users’ non-linear characteristics. At the same time, we integrate the GAN framework to guide the recommendation model training. Among them, the generator aims to make user recommendations and the discriminator is equivalent to a measurement tool which could measure the distance between the generated distribution and users’ ground distribution. Through comparison with other existing recommendation algorithms, our algorithm show better experimental performance in all indicators.

Quantum-Inspired Recommendation System with Threshold Proportion Interception

SPIN ◽  
2021 ◽  
Author(s):  
Meng Qiao ◽  
Zheng Shan ◽  
Junchao Wang ◽  
Huihui Sun ◽  
Fudong Liu
Keyword(s):  
Time Complexity ◽  
Numerical Experiments ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Industrial Applications ◽  
Constant Time ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Industrial Environments

Modern recommendation systems leverage historical behavior information to generate precise recommendation results for users. However, when the data scale of users and items is large, it is difficult to generate recommendation results in time. Tang proposed a quantum-inspired recommendation algorithm, which could solve the recommendation problem in constant time complexity. However, Tang’s approach is based on a set of assumptions which rely heavily on some empirical parameters. The time complexity for calculating parameters is high. Thus, this approach cannot be directly applied in industrial applications. In this paper, we propose a method, namely, Quantum-inspired Recommendation system with threshold Proportion Interception (QRPI), which is based on the quantum-inspired recommendation system and more suitable for industrial environments. Compared with the existing widely used recommendation algorithms, we show through numerical experiments that our solution can achieve almost the same performance with better efficiency.

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

ANALISIS PENGARUH EKSPOR TERHADAP PERTUMBUHAN EKONOMI INDONESIA

2017 ◽  
Vol 11 (1) ◽  
pp. 1-20
Author(s):  
Ari Mulianta Ginting
Keyword(s):  
Economic Growth ◽  
Export Performance ◽  
Descriptive Analysis ◽  
Correction Model ◽  
Administration System ◽  
Long Run ◽  
Short Run ◽  
Available Information ◽  
Increasing Trends ◽  
The Relationship

Ekspor merupakan salah satu faktor terjadinya peningkatan pertumbuhan ekonomi suatu negara, sejalan dengan hipotesis export-led growth (ELG). Penelitian ini menganalisis perkembangan ekspor dan pertumbuhan ekonomi Indonesia periode kuartal I 2001 sampai dengan kuartal IV 2015. Penelitian ini menggunakan analisis deskriptif dalam menggambarkan perkembangan pertumbuhan ekonomi serta ekspor dan analisis kuantitatif metode Error Correction Model (ECM) dalam menganalisis efek jangka panjang dan jangka pendek dari ekspor terhadap pertumbuhan ekonomi. Pada periode penelitian, data yang ada menunjukkan bahwa ekspor dan pertumbuhan ekonomi Indonesia sama-sama mengalami peningkatan. Hasil regresi ECM menunjukkan bahwa ekspor memiliki pengaruh yang positif dan signifikan secara statistik terhadap pertumbuhan ekonomi Indonesia, yang mendukung hipotesis bahwa ELG berlaku untuk Indonesia. Berdasarkan hasil penelitian ini, maka untuk mendorong pertumbuhan ekonomi Indonesia diperlukan peningkatan kinerja ekspor Indonesia. Peningkatan kinerja ekspor Indonesia dapat dilakukan dengan berbagai cara, salah satunya adalah dengan perbaikan sistem administrasi ekspor, peningkatan riset dan pengembangan produk Indonesia, peningkatan sarana dan prasarana infrastruktur, stabilitas nilai tukar dan perluasan pasar non tradisional, termasuk perbaikan struktur ekspor komoditas. Export is one of the factors behind the economic growth which is in line with the export-led growth hypotesis (ELG). This research analyzes the relationship between economic growth and export of Indonesia during first quarter of 2001 until fourth quarter of 2015. It employs descriptive analysis to describe export movement and economic growth during the study period and ECM model to analyze the long run and the short run effects of export on the economic growth. The available information indicated that, during the study period, both export and economic growth showed similar increasing trends. The result of the ECM model revealed that export had a positive and statistically significant relationship with the economic growth, supporting the hypotesis of ELG in Indonesia. Hence, to accelerate economic growth, efforts are required to boost the export performance in Indonesia. The Export performance can be increased by several way, such as improving the export administration system, increasing the research and development of Indonesian products, improving the facilities and infrastructure, exchange rate stability and the non-tradisional markets expansion, and including improvement of the export commodity structure.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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