Journal
DISCRETE DYNAMICS IN NATURE AND SOCIETY
Volume 2015, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2015/691781
Keywords
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Funding
- Humanity and Social Science Project of Ministry of Education of China [13YJC630041, 13YJCZH216]
- Zhejiang Provincial Natural Science Foundation of China [LQ13G020008]
- Zhejiang Provincial Education Scientific Research Project of China [Y201225624]
- Philosophy and Social Sciences Project of Taizhou of China [14GHZ02]
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In the current supply chain environment, distributed cognition theory tells us that various types of context information in which a recommendation is provided are important for e-commerce customer satisfaction management. However, traditional recommendation model does not consider the distributed and differentiated impact of different contexts on user needs, and it also lacks adaptive capacity of contextual recommendation service. Thus, a contextual information recommendation model based on distributed cognition theory is proposed. Firstly, the model analyzes the differential impact of various sensitive contexts and specific examples on user interest and designs a user interest extraction algorithm based on distributed cognition theory. Then, the sensitive contexts extracted from user are introduced into the process of collaborative filtering recommendation. The model calculates similarity among user interests. Finally, a novel collaborative filtering algorithm integrating with context and user similarity is designed. The experimental results in e-commerce and benchmark dataset show that this model has a good ability to extract user interest and has higher recommendation accuracy compared with other methods.
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