Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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Institution Author: search.filters.institutionAuthor.Kasap, Seda

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  • Conference Object
    A Roadmap for Semantifying Recommender Systems Using Preference Management
    (Springer, 2010) Tapucu, Dilek; Tekbacak, Fatih; Ünalır, Murat Osman; Kasap, Seda
    The work developed in this paper presents an innovative solution in the field of recommender systems. Our aim is to create integration architecture for improving recommendation effectiveness that obtains user preferences found implicitly in domain knowledge. This approach is divided into four steps. The first step is based on semantifying domain knowledge. In this step, domain ontology will be analyzed. The second step is to define an innovative hybrid recommendation algorithm based upon collaborative filtering and content filtering. The third step is based on preference modeling approach. And in the fourth step preference model and recommendation algorithm will be integrated. Finally, this work will be realized on Netflix movie data source. © 2011 Springer Science+Business Media B.V.
  • Conference Object
    Citation - Scopus: 5
    Performance Comparison of Combined Collaborative Filtering Algorithms for Recommender Systems
    (Institute of Electrical and Electronics Engineers Inc., 2012) Tapucu, Dilek; Kasap, Seda; Tekbacak, Fatih
    Recommender systems have a goal to make personalized recommendations by using filtering algorithms. Collaborative filtering (CF) is one of the most popular techniques for recommender systems. As usual, huge number of the datasets on the Internet increase the amount of time to work on data. This challenge enforces people to improve better algorithms for processing data with user preferences and recommending the most appropriate item to the users. In this paper, we analyze CF algorithms and present results for combined user-based/item-based CF algorithms for different size of datasets. Our goal is to show combined solution results using Loglikelihood, Spearman, Tanimoto and Pearson algorithms. The contribution is to describe which user based CF algorithms and user/item based combined CF algorithms perform better according to dataset, sparsity, execution time and k-neighborhood values. © 2012 IEEE.