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Wireless Personal Communications

, Volume 72, Issue 4, pp 1801–1826 | Cite as

The Adaptive Ontology-Based Personalized Recommender System

  • Sheng-Tzong Cheng
  • Chih-Lun Chou
  • Gwo-Jiun HorngEmail author
Article

Abstract

Recommender systems provide strategies that help users search or make decisions within the overwhelming information spaces nowadays. They have played an important role in various areas such as e-commerce and e-learning. In this paper, we propose a hybrid recommendation strategy of content-based and knowledge-based methods that are flexible for any field to apply. By analyzing the past rating records of every user, the system learns the user’s preferences. After acquiring users’ preferences, the semantic search-and-discovery procedure takes place starting from a highly rated item. For every found item, the system evaluates the Interest Intensity indicating to what degree the user might like it. Recommender systems train a personalized estimating module using a genetic algorithm for each user, and the personalized estimating model helps improve the precision of the estimated scores. With the recommendation strategies and personalization strategies, users may have better recommendations that are closer to their preferences. In the latter part of this paper, a real-world case, a movie-recommender system adopting proposed recommendation strategies, is implemented.

Keywords

Recommender systems Hybrid recommender systems Content-based filtering Knowledge-based systems Semantic web Ontologies Personalization  Genetic algorithm 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Sheng-Tzong Cheng
    • 1
  • Chih-Lun Chou
    • 1
  • Gwo-Jiun Horng
    • 1
    Email author
  1. 1.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTainanTaiwan

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