[Objective] This paper researches the flow and framework of commodity information recommender system in the absence of consumers behavior information. [Context] Recommender system is an effective means to reduce information overload. But for the overreliance on consumers behavior information, it may have the cold-start problem, and raise consumer's privacy concerns also. [Methods] With the help of commodity domain knowledge, the interactive recommender system ascertains the consumer's commodity quanticational attribute requirement according to the rough use demand, and then recommends the right product information to the consumer. [Results] A prototype system is designed for experimental study, and the results show high customer satisfactions. [Conclusions] The proposed method can solve the cold-start problem and consumer's privacy concerns to some extent.
胡新明, 罗建军, 夏火松. 基于商品领域知识的交互式推荐系统[J]. 现代图书情报技术, 2014, 30(10): 56-62.
Hu Xinming, Luo Jianjun, Xia Huosong. Research on Interactive Recommender System Based on Commodity Domain Knowledge. New Technology of Library and Information Service, 2014, 30(10): 56-62.
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