Product Recommendation Algorithm for Score Prediction Based on Joint Feature Vector Extraction

2021 ◽  
Author(s):  
Jian-hua LI ◽  
Chen-xi ZHANG ◽  
Chun-li LEI ◽  
Hong ZHANG ◽  
Lin-long CHEN
Author(s):  
Xiaoying Yao ◽  
Chunnian Liu ◽  
Yingfei Zhu

Emergency case data resources are widely distributed and heterogeneous. At the same time, the command of emergency field needs the cooperation of multiple departments. Therefore, it is urgent to establish an emergency analysis and mining platform, realize the sharing and collaboration of emergency data resources among multiple departments, and assist emergency command and scheduling. According to the actual situation of the current emergency, a similarity measure method (TCRD) is proposed to solve this problem by adding temporal information to reflect information adoption, which integrates user context information and temporal information. Firstly, the temporal information of historical adoption behavior is expressed as a binary coded characteristic matrix, and then the characteristic matrix is mapped into a feature vector by using restricted Boltzmann machine, and finally added to the similarity measurement formula. The improved TCRD method can measure the similarity more accurately, and further improve the quality of emergency information adoption recommendation results.


2012 ◽  
Vol 461 ◽  
pp. 289-292
Author(s):  
Kai Zhou

Recommender systems are becoming increasingly popular, and collaborative filtering method is one of the most important technologies in recommender systems. The ability of recommender systems to make correct predictions is fundamentally determined by the quality and fittingness of the collaborative filtering that implements them. It is currently mainly used for business purposes such as product recommendation. Collaborative filtering has two types. One is user based collaborative filtering using the similarity between users to predict and the other is item based collaborative filtering using the similarity between items. Although both of them are successfully applied in wide regions, they suffer from a fundamental problem of data sparsity. This paper gives a personalized collaborative filtering recommendation algorithm combining the item rating similarity and the item classification similarity. This method can alleviate the data sparsity problem in the recommender systems


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