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Web Content Recommender System based on Consumer Behavior Modeling
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AI
DOI：10.1109/TCE.2011.5955246.png)
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摘要
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Web surfing has become a popular activity for many consumers who not only make purchases online, but also seek relevant information on products and services before they commit to buy. The authors propose a web recommender that models user habits and behaviors by constructing a knowledge base using temporal web access patterns as input. Fuzzy logic is applied to represent real-life temporal concepts and requested resources of periodic pattern-based web access activities. The fuzzy representation is used to construct a knowledge base of the user's web access habits and behaviors, which is used to provide timely personalized recommendations to the user. The proposed approach is applicable to delivery of recommendations on consumers' portable devices because compute-intensive processing is performed offline and in advance. With the increasing availability and popularity of web-enabled consumer mobile devices, it is believed that the CE world of tomorrow will be increasingly web-oriented. Experiments conducted to evaluate the performance of the proposed approach have shown very good results(1).
Keyword:
Consumer behavior modeling
personalization
web content recommender
consumer internet application
期刊
IF:
10.9
论文数:
5.1K
被引数:
6.8K
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