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- W4200149043 abstract "Group recommendation, as a type of recommender systems, mainly recommends items to a group of users. In this paper, inspired by Bidirectional Encoder Representation from Transformers (BERT), our goal is to explore a novel BERT-based group modeling. Therefore, we devise a novel BERT-based Group Recommendation approach, namely BGR. Specifically, BERT is adopted to capture group preferences by using sentence-level embedding from a global perspective. Based on this, we then integrate aggregation representation of group users to obtain final group representation. Finally, group recommendation is achieved in the framework of neural collaborative filtering. Experiments are conducted on two real-world datasets demonstrate that our BGR outperforms the baselines." @default.
- W4200149043 created "2021-12-31" @default.
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- W4200149043 date "2021-10-29" @default.
- W4200149043 modified "2023-09-24" @default.
- W4200149043 title "BERT-based Aggregative Group Representation for Group Recommendation" @default.
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- W4200149043 doi "https://doi.org/10.1109/besc53957.2021.9635100" @default.
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