Now showing items 1-17 of 17

    • Collaborative filtering based on user trends 

      Symeonidis P; Nanopoulos A; Papadopoulos A; Manolopoulos Y (Springer, 2007)
      Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative Filtering (CF) is a succesful recommendation technique. User ratings are not expected ...
    • Collaborative filtering process in a whole new light 

      Symeonidis P; Nanopoulos A; Papadopoulos A; Manolopoulos Y (IEEE, 2006)
      Collaborative Filtering (CF) Systems are gaining widespread acceptance in recommender systems and e-commerce applications. These systems combine information retrieval and data mining techniques to provide recommendations ...
    • Collaborative filtering: Fallacies and insights in measuring similarity 

      Symeonidis P; Nanopoulos A; Papadopoulos AN; Manolopoulos Y (Universitaet Kassel, 2006)
      Nearest-neighbor collaborative filtering (CF) algorithms are gaining widespread acceptance in recommender systems and e-commerce applications. These algorithms provide recommendations for products, based on suggestions of ...
    • Collaborative recommender systems: Combining effectiveness and efficiency 

      Symeonidis P; Nanopoulos A; Papadopoulos A; Manolopoulos Y (2008)
      Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the "information ...
    • Feature-weighted user model for recommender systems 

      Symeonidis P; Nanopoulos A; Manolopoulos Y (Springer, 2007)
      Recommender systems are gaining widespread acceptance in e-commerce applications to confront the "information overload" problem. Collaborative Filtering (CF) is a successful recommendation technique, which is based on past ...
    • Justified recommendations based on content and rating data 

      Symeonidis P; Nanopoulos A; Manolopoulos Y (ACM, 2008)
      Providing justification to a recommendation gives credibility to a recommender system. Some recommender systems (Amazon.com etc.) try to explain their recommendations, in an effort to regain customer acceptance and trust. ...
    • MoviExplain: A recommender system with explanations 

      Symeonidis P; Nanopoulos A; Manolopoulos Y (ACM, 2009)
      Providing justification to a recommendation gives credibility to a recommender system. Some recommender systems (Amazon.com etc.) try to explain their recommendations, in an effort to regain customer acceptance and trust. ...
    • MusicBox: Personalized Music Recommendation Based on Cubic Analysis of Social Tags 

      Nanopoulos A; Rafailidis D; Symeonidis P; Manolopoulos Y (2010)
      Social tagging is becoming increasingly popular in music information retrieval (MIR). It allows users to tag music items like songs, albums, or artists. Social tags are valuable to MIR, because they comprise a multifaced ...
    • Nearest-biclusters collaborative filtering based on constant and coherent values 

      Symeonidis P; Nanopoulos A; Papadopoulos A; Manolopoulos Y (2008)
      Collaborative Filtering (CF) Systems have been studied extensively for more than a decade to confront the "information overload" problem. Nearest-neighbor CF is based either on similarities between users or between items, ...
    • Nearest-biclusters collaborative filtering with constant values 

      Symeonidis P; Nanopoulos A; Papadopoulos A; Manolopoulos Y (Springer, 2007)
      Collaborative Filtering (CF) Systems have been studied extensively for more than a decade to confront the "information overload" problem. Nearest-neighbor CF is based either on common user or item similarities, to form the ...
    • Providing justifications in recommender systems 

      Symeonidis P; Nanopoulos A; Manolopoulos Y (2008)
      Recommender systems are gaining widespread acceptance in e-commerce applications to confront the "information overload"problem. Providing justification to a recommendation gives credibility to a recommender system. Some ...
    • Recommender systems for social tagging systems 

      Marinho LB; Hotho A; Jäschke R; Nanopoulos A; Rendle S; Schmidt-Thieme L; Stumme G; Symeonidis P (Springer Science & Business Media, 2012)
      Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize ...
    • Scalable collaborative filtering based on latent semantic indexing 

      Symeonidis P; Nanopoulos A; Papadopoulos A; Manolopoulos Y (AAAI, 2006)
      Nearest-neighbor collaborative filtering (CF) algorithms are gaining widespread acceptance in recommender systems and e-commerce applications. User ratings are not expected to be independent, as users follow trends of ...
    • Social tagging recommender systems 

      Marinho LB; Nanopoulos A; Schmidt-Thieme L; Jäschke R; Hotho A; Stumme G; Symeonidis P (Springer US, 2011)
      The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while ...
    • Tag recommendations based on tensor dimensionality reduction 

      Symeonidis P; Nanopoulos A; Manolopoulos Y (ACM, 2008)
      Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags ...
    • Ternary semantic analysis of social tags for personalized music recommendation 

      Symeonidis P; Ruxanda M; Nanopoulos A; Manolopoulos Y (Drexel University, 2008)
      Social tagging is the process by which many users add metadata in the form of keywords, to annotate information items. In case of music, the annotated items can be songs, artists, albums. Current music recommenders which ...
    • A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis 

      Symeonidis P; Nanopoulos A; Manolopoulos Y (2010)
      Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different ...