A hybrid collaborative filtering recommendation model based on complex attribute of goods

ZHOU Lan-feng; MA Shuang-ke; FU Zheng; ZHANG Qing

doi:10.3969/j.issn.1000-5641.2017.05.014

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Sep. 2017

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ZHOU Lan-feng, MA Shuang-ke, FU Zheng, ZHANG Qing. A hybrid collaborative filtering recommendation model based on complex attribute of goods[J]. Journal of East China Normal University (Natural Sciences), 2017, (5): 154-161, 185. doi: 10.3969/j.issn.1000-5641.2017.05.014

Citation:

ZHOU Lan-feng, MA Shuang-ke, FU Zheng, ZHANG Qing. A hybrid collaborative filtering recommendation model based on complex attribute of goods[J]. Journal of East China Normal University (Natural Sciences), 2017, (5): 154-161, 185. doi: 10.3969/j.issn.1000-5641.2017.05.014

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A hybrid collaborative filtering recommendation model based on complex attribute of goods

doi: 10.3969/j.issn.1000-5641.2017.05.014

School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China

Received Date: 2017-06-19
Publish Date: 2017-09-25

Abstract

Abstract

Collaborative filtering as the most widely used, the most recommendation algorithm, the shortcomings inherent in the data sparse, cold startpoor data quality and others, and few studies based on commodity price to improve the prediction accuracy. At the same time, facing the full e-commerce market network Navy, the ratings and reviews also indirectly led to the predict a decline in accuracy. Therefore, this paper comprehensive consideration of the user subjective ratings and objective product score, and on this basis, combined with situation pre filtering, social network theory and expert opinions put forward a hybrid collaborative filtering recommendation model, to some extent alleviate the above shortcomings. And through experiment with real online car sales data, the model has higher forecast accuracy than the traditional collaborative filtering, and is more suitable for the commodity with complex attributes.
- collaborative filtering,
- context,
- complex attribute,
- personalized recommendation

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References(17)

References

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