Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (27)
  • Open Access


    A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations

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

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 661-675, 2022, DOI:10.32604/iasc.2022.020132

    Abstract Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, implicit similarity, similarity transitivity and… More >

  • 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


    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


    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


    Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

    Thavavel Vaiyapuri*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 487-503, 2021, DOI:10.32604/cmc.2021.015998

    Abstract The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques—including collaborative filtering (CF), which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems, preventing… More >

  • Open Access


    A Novel Collaborative Filtering Algorithm and Its Application for Recommendations in E-Commerce

    Jie Zhang1,5, Juan Yang2,*, Li Wang3, Yizhang Jiang4, Pengjiang Qian4, Yuan Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1275-1291, 2021, DOI:10.32604/cmes.2021.012112

    Abstract With the rapid development of the Internet, the amount of data recorded on the Internet has increased dramatically. It is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of data. In this study, we propose a novel collaborative filtering algorithm for generating recommendations in e-commerce. This study has two main innovations. First, we propose a mechanism that embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or item. Second, we propose a novel collaborative filtering algorithm by injecting… More >

  • Open Access


    An Attention-Based Friend Recommendation Model in Social Network

    Chongchao Cai1, 2, Huahu Xu1, *, Jie Wan2, Baiqing Zhou2, Xiongwei Xie3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2475-2488, 2020, DOI:10.32604/cmc.2020.011693

    Abstract In social networks, user attention affects the user’s decision-making, resulting in a performance alteration of the recommendation systems. Existing systems make recommendations mainly according to users’ preferences with a particular focus on items. However, the significance of users’ attention and the difference in the influence of different users and items are often ignored. Thus, this paper proposes an attention-based multi-layer friend recommendation model to mitigate information overload in social networks. We first constructed the basic user and item matrix via convolutional neural networks (CNN). Then, we obtained user preferences by using the relationships between users and items, which were later… 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 >

  • Open Access


    Knowledge Graph Representation Reasoning for Recommendation System

    Tao Li, Hao Li*, Sheng Zhong, Yan Kang, Yachuan Zhang, Rongjing Bu, Yang Hu

    Journal of New Media, Vol.2, No.1, pp. 21-30, 2020, DOI:10.32604/jnm.2020.09767

    Abstract In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms, we propose an efficient KGRS model. KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm, then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning, then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning. Finally, through the full connection layer and sigmoid function to get the prediction ratings, and the items are sorted… More >

  • Open Access


    A Recommendation Approach Based on Product Attribute Reviews: Improved Collaborative Filtering Considering the Sentiment Polarity

    Min Cao1, Sijing Zhou1, Honghao Gao1,2,3

    Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 595-604, 2019, DOI:10.31209/2019.100000114

    Abstract Recommender methods using reviews have become an area of active research in e-commerce systems. The use of auxiliary information in reviews as a way to effectively accommodate sparse data has been adopted in many fields, such as the product field. The existing recommendation methods using reviews typically employ aspect preference; however, the characteristics of product reviews are not considered adequate. To this end, this paper proposes a novel recommendation approach based on using product attributes to improve the efficiency of recommendation, and a hybrid collaborative filtering is presented. The product attribute model and a new recommendation ranking formula are introduced… More >

Displaying 11-20 on page 2 of 27. Per Page