A group recommendation model integrating time series features
Traditional group recommendation has such problems as ineffective predefined strategy, neglect of the interaction between users and projects, and failure to capture the migration of user preferences caused by the passage of time. In response to the above problems, a group recommendation model TAGR (...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of XPU
2024-02-01
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| Series: | Xi'an Gongcheng Daxue xuebao |
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| Online Access: | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1440 |
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| _version_ | 1849727787377623040 |
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| author | ZHU Xinjuan XIONG Yilun |
| author_facet | ZHU Xinjuan XIONG Yilun |
| author_sort | ZHU Xinjuan |
| collection | DOAJ |
| description | Traditional group recommendation has such problems as ineffective predefined strategy, neglect of the interaction between users and projects, and failure to capture the migration of user preferences caused by the passage of time. In response to the above problems, a group recommendation model TAGR (time-attentive group recommendation) that integrates time series and attention mechanisms was proposed. Firstly, high similarity groups were divided through hierarchical clustering. Secondly, a time series model was introduced to capture the process of user preference transfer, obtain the interest preferences of user behavior at each moment, and aggregate the interest preferences at each moment as user preferences. Finally, with attention mechanism, user weights were obtained for preference fusion to represent group preferences, serving as the input of the recommendation model. By comparing with NCF, AGREE and other models on the Goodbook and MovieLens datasets, the proposed model TAGR has been significantly improved in both normalized discount cumulative gain and hit rate. |
| format | Article |
| id | doaj-art-e91e5ae0c43d49fcbed88cdf60613c6c |
| institution | DOAJ |
| issn | 1674-649X |
| language | zho |
| publishDate | 2024-02-01 |
| publisher | Editorial Office of Journal of XPU |
| record_format | Article |
| series | Xi'an Gongcheng Daxue xuebao |
| spelling | doaj-art-e91e5ae0c43d49fcbed88cdf60613c6c2025-08-20T03:09:45ZzhoEditorial Office of Journal of XPUXi'an Gongcheng Daxue xuebao1674-649X2024-02-0138110511210.13338/j.issn.1674-649x.2024.01.014A group recommendation model integrating time series featuresZHU Xinjuan0XIONG Yilun1School of Computer Science/The Shaanxi Key Laboratory of Clothing Intelligence, Xi’an Polytechnic University, Xi’an 710048, ChinaSchool of Computer Science/The Shaanxi Key Laboratory of Clothing Intelligence, Xi’an Polytechnic University, Xi’an 710048, ChinaTraditional group recommendation has such problems as ineffective predefined strategy, neglect of the interaction between users and projects, and failure to capture the migration of user preferences caused by the passage of time. In response to the above problems, a group recommendation model TAGR (time-attentive group recommendation) that integrates time series and attention mechanisms was proposed. Firstly, high similarity groups were divided through hierarchical clustering. Secondly, a time series model was introduced to capture the process of user preference transfer, obtain the interest preferences of user behavior at each moment, and aggregate the interest preferences at each moment as user preferences. Finally, with attention mechanism, user weights were obtained for preference fusion to represent group preferences, serving as the input of the recommendation model. By comparing with NCF, AGREE and other models on the Goodbook and MovieLens datasets, the proposed model TAGR has been significantly improved in both normalized discount cumulative gain and hit rate.http://journal.xpu.edu.cn/en/#/digest?ArticleID=1440group recommendationtime serieshierarchical clusteringneural networkattention mechanism |
| spellingShingle | ZHU Xinjuan XIONG Yilun A group recommendation model integrating time series features Xi'an Gongcheng Daxue xuebao group recommendation time series hierarchical clustering neural network attention mechanism |
| title | A group recommendation model integrating time series features |
| title_full | A group recommendation model integrating time series features |
| title_fullStr | A group recommendation model integrating time series features |
| title_full_unstemmed | A group recommendation model integrating time series features |
| title_short | A group recommendation model integrating time series features |
| title_sort | group recommendation model integrating time series features |
| topic | group recommendation time series hierarchical clustering neural network attention mechanism |
| url | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1440 |
| work_keys_str_mv | AT zhuxinjuan agrouprecommendationmodelintegratingtimeseriesfeatures AT xiongyilun agrouprecommendationmodelintegratingtimeseriesfeatures AT zhuxinjuan grouprecommendationmodelintegratingtimeseriesfeatures AT xiongyilun grouprecommendationmodelintegratingtimeseriesfeatures |