Yazar "Al-Sabaawi, Ali Mohsin Ahmed" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • [ X ]
    Öğe
    A novel overlapping method to alleviate the cold-start problem in recommendation systems
    (World Scientific, 2021) Al-Sabaawi, Ali Mohsin Ahmed; Karacan, Hacer; Yenice, Yusuf Erkan
    Recommendation systems (RSs) are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The main objective of RSs is to tool up users with desired items that meet their preferences. A major problem in RSs is called: "cold-start"; it is a potential problem called so in computer-based information systems which comprises a degree of automated data modeling. Particularly, it concerns the issue in which the system cannot draw any inferences nor have it yet gathered sufficient information about users or items. Since RSs performance is substantially limited by cold-start users and cold-start items problems; this research study takes the route for a major aim to attenuate users' cold-start problem. Still in the process of researching, sundry studies have been conducted to tackle this issue by using clustering techniques to group users according to their social relations, their ratings or both. However, a clustering technique disregards a variety of users' tastes. In this case, the researcher has adopted the overlapping technique as a tool to deal with the clustering technique's defects. The advantage of the overlapping technique excels over others by allowing users to belong to multi-clusters at the same time according to their behavior in the social network and ratings feedback. On that account, a novel overlapping method is presented and applied. This latter is executed by using the partitioning around medoids (PAM) algorithm to implement the clustering, which is achieved by means of exploiting social relations and confidence values. After acquiring users' clusters, the average distances are computed in each cluster. Thereafter, a content comparison is made regarding the distances between every user and the computed distances of the clusters. If the comparison result is less than or equal to the average distance of a cluster, a new user is added to this cluster. The singular value decomposition plus (SVD++) method is then applied to every cluster to compute predictions values. The outcome is calculated by computing the average of mean absolute error (MAE) and root mean square error (RMSE) for every cluster. The model is tested by two real world datasets: Ciao and FilmTrust. Ultimately, findings have exhibited a great deal of insights on how the proposed model outperformed a number of the state-of-the-art studies in terms of prediction accuracy.
  • Küçük Resim
    Öğe
    Adopting several models to alleviate sparsity and cold-start problems using data mining techniques for recommendation systems
    (Aksaray Üniversitesi Fen Bilimleri Enstitüsü, 2021) Al-Sabaawi, Ali Mohsin Ahmed; Yenice, Yusuf Erkan; Karacan, Hacer
    The development of Web 2.0 and the rapid growth of available data have led to the evolution of multiple systems among them the Recommendation Systems (RSs) which can handle the information overload. Due to the fact that, RSs performance is substantially limited by sparsity and cold-start problems; this research study takes the route for a major objective to attenuate these problems. To realize this objective, four data mining techniques are proposed namely: multi-steps resource allocation- singular value decomposition (MSRA-SVD), MSRA-SVD++, clustering community detection, and overlapping community detection. The first two models are dedicated to tackle the data sparsity problem whereas the rest models are adopted to alleviate cold-start users' problem. The core strategy of the first two models is to use the (MSRA) method to identify hidden relations in social network. the MSRA method is applied to determine the probability of their relation. If the probability exceeds a threshold, a new relationship will be established. For the second model (MSRA-SVD++), an implicit feedback source is exploited as an additional source of information, which can be extracted via rating information. Additionally, clustering and overlapping models are adopted to overcome cold-start user problem. The main idea of these models is to apply a clustering technique to group users into several communities. In order to attain that, explicit and implicit social relations with the confidence values are integrated to compute distance values. Additionally, the partitioning around medoids (PAM) clustering algorithm is adopted. Later, for the last model, the average distances of all clusters are computed. For all users, the distance between users and the center node of a particular cluster is computed. If the distance is less than the average of this cluster, a new user for this cluster will be added. Moreover, the SVD++ method is employed for each cluster to compute the prediction value. The proposed models are evaluated through the usage of three real-world datasets. Ultimately, findings exhibited a great deal of insights on how the proposed models outperformed a number of the state-of-the-art studies in terms of prediction accuracy.
  • [ X ]
    Öğe
    SVD++ and clustering approaches to alleviating the cold-start problem for recommendation systems
    (ICIC International, 2021) Al-Sabaawi, Ali Mohsin Ahmed; Karacan, Hacer; Yenice, Yusuf Erkan
    Recommendation systems provide a solution to tackle information overload problem. These systems have several limitations, one of which is cold-start users. In this article, a new method is proposed to overcome the cold-start user problem. The main idea of this study is to apply a clustering technique using trust relations and rating information to compute the weights. First, the implicit relations are determined, and then the similarity is computed for each pair of explicit and implicit relations. Second, confidence values are determined through an information rating by dividing the number of common items for each pair of users by the number of items that have been rated by the first user of this pair. Furthermore, the similarity and confidence values are integrated to produce weight values, and then the distance values are inferred. Additionally, the partitioning around medoids clustering algorithm is adopted to cluster the users into groups according to their computed distances. Moreover, the Singular Value Decomposition Plus (SVD++) method is employed for each cluster to predict the items for cold-start users. Eventually, the proposed method is evaluated with two real-world datasets. The results reveal that the proposed method outperforms the state-of-the-art trust methods in terms of prediction accuracy.