|Table of Contents|

FastGR:A Group Recommendation Algorithm Based on Neural Collaborative Filtering(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年02期
Page:
29-34
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
FastGR:A Group Recommendation Algorithm Based on Neural Collaborative Filtering
Author(s):
Shang Wenqian1; 2; Cao Yuan1; 2
(1.State Key Laboratory of Media Convergence and Communication,Communication University of China,Beijing 100024,China)(2.School of Computer Science and Cyber Sciences,Communication University of China,Beijing 100024,China)
Keywords:
group recommendation; convolutional neural network; deep learning; preference fusion; neural collaborative filtering
PACS:
TP181
DOI:
10.3969/j.issn.1672-1292.2022.02.005
Abstract:
The key problem of group recommendation is how to integrate the different preferences of group members to meet the needs of all members. The group recommendation algorithm based on neural collaborative filtering framework and attention mechanism dynamically learns fusion strategy from the data,significantly improving the recommendation effect compared with the traditional predefined strategy based method,but the model training and infer time is longer. In order to achieve the convergence of preferences,we reconstruct the group preference fusion module by adopting the convolution neural network to extract the feature of the group members. Experiments on open data sets show that the algorithm in this paper has better accuracy and improved the training speed by 14 times than that of the current algorithm.

References:

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