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Search Results (19)
  • Open Access


    Effective Hybrid Content-Based Collaborative Filtering Approach for Requirements Engineering

    Qusai Y. Shambour*, Abdelrahman H. Hussein, Qasem M. Kharma, Mosleh M. Abualhaj

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 113-125, 2022, DOI:10.32604/csse.2022.017221

    Abstract Requirements engineering (RE) is among the most valuable and critical processes in software development. The quality of this process significantly affects the success of a software project. An important step in RE is requirements elicitation, which involves collecting project-related requirements from different sources. Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements. However, the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately. Recommender systems have been widely recognized as an effective solution to such problem. Accordingly, this study proposes an effective hybrid content-based collaborative… More >

  • Open Access


    Intelligent Nutrition Diet Recommender System for Diabetic’s Patients

    Nadia Tabassum1, Abdul Rehman2, Muhammad Hamid3,*, Muhammad Saleem4, Saadia Malik5, Tahir Alyas2

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 319-335, 2021, DOI:10.32604/iasc.2021.018870

    Abstract Diabetes is one of the ever-increasing menace crippling millions of people worldwide. It is an independent risk factor for many cardiovascular diseases including medium and small vessels and results in heart attack, stroke, kidney failure, blindness, and lower-limb amputations. According to a World Health Organization (WHO) report estimated 1.6 million deaths were the direct result of diabetes. Nutrition plays a vital role in diabetes management alongside physical activity, drugs, and insulin. Weight management can help to avert or delay at pre-diabetic stages. This research work explains the features of the Nutrition Diet Expert System (NDES), which will preferably be used… More >

  • Open Access


    Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN

    Mohanad Al-Ghobari1, Amgad Muneer2,*, Suliman Mohamed Fati3

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1553-1570, 2021, DOI:10.32604/cmc.2021.016348

    Abstract Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites, accommodation, and food according to their interests. This objective makes it harder for tourists to decide and plan where to go and what to do. Aside from hiring a local guide, an option which is beyond most travelers’ budgets, the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user reviews. Therefore, this work utilizes the prevalent recommender systems and mobile app technologies to overcome this issue. Accordingly, this study proposes location-aware personalized… More >

  • Open Access


    Recommender System for Configuration Management Process of Entrepreneurial Software Designing Firms

    Muhammad Wajeeh Uz Zaman1, Yaser Hafeez1, Shariq Hussain2, Haris Anwaar3, Shunkun Yang4,*, Sadia Ali1, Aaqif Afzaal Abbasi2, Oh-Young Song5

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2373-2391, 2021, DOI:10.32604/cmc.2021.015112

    Abstract The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers worldwide. This problem is addressed by the software development industry by software product line (SPL) practices that employ feature models. However, optimal feature selection based on user requirements is a challenging task. Thus, there is a requirement to resolve the challenges of software development, to increase satisfaction and maintain high product quality, for massive customer needs within limited resources. In this work, we propose a recommender system for the development team and clients to increase productivity and quality by utilizing historical information and… More >

  • Open Access


    Recommender Systems Based on Tensor Decomposition

    Zhoubao Sun1,*, Xiaodong Zhang1, Haoyuan Li1, Yan Xiao2, Haifeng Guo3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 621-630, 2021, DOI:10.32604/cmc.2020.012593

    Abstract Recommender system is an effective tool to solve the problems of information overload. The traditional recommender systems, especially the collaborative filtering ones, only consider the two factors of users and items. While social networks contain abundant social information, such as tags, places and times. Researches show that the social information has a great impact on recommendation results. Tags not only describe the characteristics of items, but also reflect the interests and characteristics of users. Since the traditional recommender systems cannot parse multi-dimensional information, in this paper, a tensor decomposition model based on tag regularization is proposed which incorporates social information… More >

  • Open Access


    Adversarial Attacks on Content-Based Filtering Journal Recommender Systems

    Zhaoquan Gu1, Yinyin Cai1, Sheng Wang1, Mohan Li1, *, Jing Qiu1, Shen Su1, Xiaojiang Du1, Zhihong Tian1

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1755-1770, 2020, DOI:10.32604/cmc.2020.010739

    Abstract Recommender systems are very useful for people to explore what they really need. Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers. In order to improve the efficiency of selecting the most suitable journals for publishing their works, journal recommender systems (JRS) can automatically provide a small number of candidate journals based on key information such as the title and the abstract. However, users or journal owners may attack the system for their own purposes. In this paper, we discuss about the adversarial attacks against content-based filtering JRS. We… More >

  • Open Access


    Context-Aware Collaborative Filtering Framework for Rating Prediction Based on Novel Similarity Estimation

    Waqar Ali1, 2, Salah Ud Din1, Abdullah Aman Khan1, Saifullah Tumrani1, Xiaochen Wang1, Jie Shao1, 3, *

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1065-1078, 2020, DOI:10.32604/cmc.2020.010017

    Abstract Recommender systems are rapidly transforming the digital world into intelligent information hubs. The valuable context information associated with the users’ prior transactions has played a vital role in determining the user preferences for items or rating prediction. It has been a hot research topic in collaborative filtering-based recommender systems for the last two decades. This paper presents a novel Context Based Rating Prediction (CBRP) model with a unique similarity scoring estimation method. The proposed algorithm computes a context score for each candidate user to construct a similarity pool for the given subject user-item pair and intuitively choose the highly influential… More >

  • Open Access


    Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network

    Yong Yu1, Yongjun Luo1, Tong Li2, Shudong Li3, *, Xiaobo Wu4, Jinzhuo Liu1, *, Yu Jiang3, *

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 489-507, 2020, DOI:10.32604/cmc.2020.07616

    Abstract Personalized recommendation algorithms, which are effective means to solve information overload, are popular topics in current research. In this paper, a recommender system combining popularity and novelty (RSCPN) based on one-mode projection of weighted bipartite network is proposed. The edge between a user and item is weighted with the item’s rating, and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users. RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in… More >

  • Open Access


    An Entity-Association-Based Matrix Factorization Recommendation Algorithm

    Gongshen Liu1, Kui Meng1,*, Jiachen Ding1, Jan P. Nees1, Hongyi Guo1, Xuewen Zhang1

    CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 101-120, 2019, DOI:10.32604/cmc.2019.03898

    Abstract Collaborative filtering is the most popular approach when building recommender systems, but the large scale and sparse data of the user-item matrix seriously affect the recommendation results. Recent research shows the user’s social relations information can improve the quality of recommendation. However, most of the current social recommendation algorithms only consider the user's direct social relations, while ignoring potential users’ interest preference and group clustering information. Moreover, project attribute is also important in item rating. We propose a recommendation algorithm which using matrix factorization technology to fuse user information and project information together. We first detect the community structure using… More >

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