Hybrid Recommendation Algorithm Based on Hamming Clustering for User's Access Log and Weighted User Behavior

Author(s):  
Tao Li ◽  
Yan Chen ◽  
Guoqing Zhu
2018 ◽  
Vol 232 ◽  
pp. 01011
Author(s):  
Xing-Hua Lu ◽  
Ming-Yuan Liu ◽  
Hao-Hong Huang ◽  
Hong-Yu Wu

The intelligent recommendation ability of social networks is improved, a hybrid recommendation algorithm is proposed based on swarm intelligence for users' potential features in social networks. The feature extraction model of social network users is constructed, and the potential features and associated information of social network users are divided by swarm intelligence optimization technology, and the user features are learned by swarm intelligence and association rules mining. The related information of recommended items in social network is obtained, and the improvement of user item feature recommendation algorithm of social network is realized. The simulation results show that the proposed algorithm can effectively improve the accurate delivery rate of user feature recommendation in social network, and the hybrid recommendation ability for user behavior is strong, the network overhead is stable and the performance is superior


2021 ◽  
pp. 1-13
Author(s):  
Yuxuan Gao ◽  
Haiming Liang ◽  
Bingzhen Sun

With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform.


Procedia CIRP ◽  
2019 ◽  
Vol 83 ◽  
pp. 490-494 ◽  
Author(s):  
Yonghong Tian ◽  
Bing Zheng ◽  
Yanfang Wang ◽  
Yue Zhang ◽  
Qi Wu

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