Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (25)
  • Open Access


    Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph

    Donglei Lu1, Dongjie Zhu2,*, Haiwen Du3, Yundong Sun3, Yansong Wang2, Xiaofang Li4, Rongning Qu4, Ning Cao1, Russell Higgs5

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1133-1146, 2022, DOI:10.32604/csse.2022.021525

    Abstract The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to the user based on the known historical interaction data of the target user. Furthermore, the combination of the recommended algorithm based on collaborative filtration and other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model (CoFM) is one representative research. CoFM, a fusion recommendation model combining the collaborative filtering model FM and the graph embedding model TransE, introduces the information of many entities and their relations in the knowledge graph into… More >

  • Open Access


    Generating A New Shilling Attack for Recommendation Systems

    Pradeep Kumar Singh1, Pijush Kanti Dutta Pramanik1, Madhumita Sardar1, Anand Nayyar2,3,*, Mehedi Masud4, Prasenjit Choudhury1

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2827-2846, 2022, DOI:10.32604/cmc.2022.020437

    Abstract A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing. To keep the recommendation systems reliable, authentic, and superior, the security of these systems is very crucial. Though the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks, in this paper, we prove that they fail to detect a new or unknown attack. We develop a new attack model, named Obscure attack, with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as intended. The Obscure attack is able to… More >

  • Open Access


    Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

    Lakshmi Palaniappan1,*, K. Selvaraj2

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 903-917, 2022, DOI:10.32604/csse.2022.020670

    Abstract Recommendation systems are going to be an integral part of any E-Business in near future. As in any other E-business, recommendation systems also play a key role in the travel business where the user has to be recommended with a restaurant that best suits him. In general, the recommendations to a user are made based on similarity that exists between the intended user and the other users. This similarity can be calculated either based on the similarity between the user profiles or the similarity between the ratings made by the users. First phase of this work concentrates on experimentally analyzing… More >

  • Open Access


    An Auction-Based Recommender System for Over-The-Top Platform

    Hameed AlQaheri1,*, Anjan Bandyopadhay2, Debolina Nath2, Shreyanta Kar2, Arunangshu Banerjee2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5285-5304, 2022, DOI:10.32604/cmc.2022.021631

    Abstract In this era of digital domination, it is fit to say that individuals are more inclined towards viewership on online platforms due to the wide variety and the scope of individual preferences it provides. In the past few years, there has been a massive growth in the popularity of Over-The-Top platforms, with an increasing number of consumers adapting to them. The Covid-19 pandemic has also caused the proliferation of these services as people are restricted to their homes. Consumers are often in a dilemma about which subscription plan to choose, and this is where a recommendation system makes their task… More >

  • Open Access


    Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

    Zejun Yang1, Denghui Xia1, Jin Liu1, Chao Zheng2, Yanzhen Qu1,3,4, Yadang Chen1, Chengjun Zhang1,2,3,*

    Journal on Internet of Things, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jiot.2021.015401

    Abstract Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity. The internal similarity of users is combined to modify the recommendation score to make… More >

  • Open Access


    A Novel IoT Application Recommendation System Using Metaheuristic Multi-Criteria Analysis

    Mohammed Hayder Kadhim, Farhad Mardukhi*

    Computer Systems Science and Engineering, Vol.37, No.2, pp. 149-158, 2021, DOI:10.32604/csse.2021.014608

    Abstract There are a variety of Internet of Things (IoT) applications that cover different aspects of daily life. Each of these applications has different criteria and sub-criteria, making it difficult for the user to choose. This requires an automated approach to select IoT applications by considering criteria. This paper presents a novel recommendation system for presenting applications on the IoT. First, using the analytic hierarchy process (AHP), a multi-layer architecture of the criteria and sub-criteria in IoT applications is presented. This architecture is used to evaluate and rank IoT applications. As a result, finding the weight of the criteria and sub-criteria… More >

  • Open Access


    Task-Oriented Battlefield Situation Information Hybrid Recommendation Model

    Chunhua Zhou*, Jianjing Shen, Xiaofeng Guo, Zhenyu Zhou

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 127-141, 2021, DOI:10.32604/iasc.2021.012532

    Abstract In the process of interaction between users and battlefield situation information, combat tasks are the key factors that affect users’ information selection. In order to solve the problems of battlefield situation information recommendation (BSIR) for combat tasks, we propose a task-oriented battlefield situation information hybrid recommendation model (TBSI-HRM) based on tensor factorization and deep learning. In the model, in order to achieve high-precision personalized recommendations, we use Tensor Factorization (TF) to extract correlation relations and features from historical interaction data, and use Deep Neural Network (DNN) to learn hidden feature vectors of users, battlefield situation information and combat tasks from… More >

  • Open Access


    Battlefield Situation Information Recommendation Based on Recall-Ranking

    Chunhua Zhou*, Jianjing Shen, Yuncheng Wang, Xiaofeng Guo

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1429-1440, 2020, DOI:10.32604/iasc.2020.011757

    Abstract With the rapid development of information technology, battlefield situation data presents the characteristics of “4V” such as Volume, Variety, Value and Velocity. While enhancing situational awareness, it also brings many challenges to battlefield situation information recommendation (BSIR), such as big data volume, high timeliness, implicit feedback and no negative feedback. Focusing on the challenges faced by BSIR, we propose a two-stage BSIR model based on deep neural network (DNN). The model utilizes DNN to extract the nonlinear relationship between the data features effectively, mine the potential content features, and then improves the accuracy of recommendation. These two stages are the… More >

  • Open Access


    SRI-XDFM: A Service Reliability Inference Method Based on Deep Neural Network

    Yang Yang1,*, Jianxin Wang1, Zhipeng Gao1, Yonghua Huo2, Xuesong Qiu1

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1459-1475, 2020, DOI:10.32604/iasc.2020.011688

    Abstract With the vigorous development of the Internet industry and the iterative updating of web service technologies, there are increasing web services with the same or similar functions in the ocean of platforms on the Internet. The issue of selecting the most reliable web service for users has received considerable critical attention. Aiming to solve this task, we propose a service reliability inference method based on deep neural network (SRI-XDFM) in this article. First, according to the pattern of the raw data in our scenario, we improve the performance of embedding by extracting self-correlated information with the help of character encoding… More >

  • Open Access


    A Novel Fuzzy Rough Sets Theory Based CF Recommendation System

    C. Raja Kumar1, VE. Jayanthi2

    Computer Systems Science and Engineering, Vol.34, No.3, pp. 123-129, 2019, DOI:10.32604/csse.2019.34.123

    Abstract Collaborative Filtering (CF) is one of the popular methodology in recommender systems. It suffers from the data sparsity problem, recommendation inaccuracy and big-error in predictions. In this paper, the efficient advisory tool is implemented for the younger generation to choose their right career based on their knowledge. It acquires the notions of indiscernible relation from Fuzzy Rough Sets Theory (FRST) and propose a novel algorithm named as Fuzzy Rough Set Theory Based Collaborative Filtering Algorithm (FRSTBCF). To evaluate the model, data is prepared using the cross validation method. Based on that, ratings are evaluated by calculating the MAE (mean average… More >

Displaying 11-20 on page 2 of 25. Per Page