The adaptive ontology-based personalized recommender system

Sheng Tzong Cheng, Chih Lun Chou, Gwo Jiun Horng

研究成果: Article同行評審

17 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)1801-1826
期刊Wireless Personal Communications
出版狀態Published - 2013 10月

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 電氣與電子工程


深入研究「The adaptive ontology-based personalized recommender system」主題。共同形成了獨特的指紋。