NEWER: A system for NEuro-fuzzy WEb Recommendation |
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Authors: | G Castellano AM Fanelli MA Torsello |
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Affiliation: | 1. TED University, Computer Engineering Department, Ankara, Turkey;2. Independent Researcher, Barcelona, Spain;3. Bilkent University, Computer Engineering Department, Ankara, Turkey;4. NTENT, Carlsbad, CA, USA;1. MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University, Guangzhou 510632, China;2. Earthquake Engineering Research & Test Center, Guangzhou University, Guangzhou 510405, China;3. Department of Mechanics, Sun Yat-sen University, Guangzhou 510275, China |
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Abstract: | In the era of the Web, there is urgent need for developing systems able to personalize the online experience of Web users on the basis of their needs. Web recommendation is a promising technology that attempts to predict the interests of Web users, by providing them with information and/or services that they need without explicitly asking for them. In this paper we propose NEWER, a usage-based Web recommendation system that exploits the potential of Computational Intelligence techniques to dynamically suggest interesting pages to users according to their preferences. NEWER employs a neuro-fuzzy approach in order to determine categories of users sharing similar interests and to discover a recommendation model as a set of fuzzy rules expressing the associations between user categories and relevances of pages. The discovered model is used by a online recommendation module to determine the list of links judged relevant for users. The results obtained on both synthetic and real-world data show that NEWER is effective for recommendation, leading to a quality of the generated recommendations comparable and often significantly better than those of other approaches employed for the comparison. |
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