Yazar "Karacan, Hacer" 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.
  • [ 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.
  • Küçük Resim
    Öğe
    Two models based on social relations and SVD++ method for recommendation system
    (International Association of Online Engineering, 2021) Al Sabaawi, Ali M. Ahmed; Karacan, Hacer; Yenice, Yusuf Erkan
    Recently, Recommender Systems (RSs) have attracted many researchers whose goal is to improve the performance of the prediction accuracy of recommendation systems by alleviating RSs drawbacks. The most common limitations are sparsity and the cold-start user problems. This article proposes two models to mitigate the effects of these limitations. The proposed models exploit five sources of information: rating information, which involves two sources, namely explicit and implicit, which can be extracted via users’ ratings, and two types of social relations: explicit and implicit relations, the last source is confidence values that are included in the first model only. The whole sources are combined into the Singular Value Decomposition plus (SVD++) method. First, to extract implicit relations, each non-friend pair of users, the Multi-Steps Resource Allocation (MSRA) method is adopted to compute the probability of being friends. If the probability has accepted value which exceeds a threshold, an implicit relationship will be created. Second, the similarity of explicit and implicit social relationships for each pair of users is computed. Regarding the first model, a confidence value between each pair of users is computed by dividing the number of common items by the total number of items which have also rated by the first user of this pair. The confidence values are combined with the similarity values to produce the weight factor. Furthermore, the weight factor, explicit, and implicit feedback information are integrated into the SVD++ method to compute the missing prediction values. Additionally, three standard datasets are utilized in this study, namely Last. Fm, Ciao, and FilmTrust, to evaluate our models. The experimental results have revealed that the proposed models outperformed state-of-the-art approaches in terms of accuracy.