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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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of XPU
2024-02-01
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| Series: | Xi'an Gongcheng Daxue xuebao |
| Subjects: | |
| Online Access: | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1440 |
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| Summary: | 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. |
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| ISSN: | 1674-649X |