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Knowledge graph-based graph neural network models for multi-perspective modeling of group preferences

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Abstract

The purpose of group recommendation is to recommend items that all users in a group may like; therefore, the modeling target of group recommendation is not individual preference features, but group preference features, which are very complex in the context of online social platforms. In this paper, in order to better model group preferences, We propose the model Knowledge graph-based Multi-view Attention Group Recommendation (KMAGR) to model the group preference relationship in three aspects: 1) adding knowledge mapping relationships as side information to the model; 2) using attention mechanisms and graph neural network structures to model group purchase intentions; 3) in addition to modeling group preferences from the user’s perspective, we use group-user and group-intent multiple perspectives to model group preferences. We conducted experiments on two real online social datasets, and the experimental results proved that KMAGR outperformed other state-of-the-art models in group recommendation. Adding knowledge graph information and identifying group intent to the group recommendation system can greatly improve the effectiveness of group recommendation, while the critical path aggregation mechanism improves the explainability of recommendation results.

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  1. https://www.pinterest.com.

  2. https://www.bilibili.com

  3. https://pytorch.org.

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  1. School of Economics and Management, Shanghai Maritime University, Shanghai, 200000, China

    Zongyu Wang & Yan Li

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  2. Yan Li
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Appendix A An overview of used benchmark datasets

Appendix A An overview of used benchmark datasets

We conducted a study of the dataset used in group recommendations. After investigating the relevant papers, we divided the datasets used in group recommendation into three cases. Firstly, dataset transformation in personalized recommendation is the mainstream of group recommendation dataset used, such as Movie Lens, Last FM, Amazon-Book and so on. The main reason for choosing these datasets is their relative stability and high level of information-rich recognition, and because the number of groups is usually controlled in group recommendation to facilitate the experiment, there is a considerable human control factor for the control of groups in these datasets [1, 7, 9, 31, 33, 34]. Secondly, part of the research is to use competition-related datasets, which usually come from the real business problems of some companies, and have a certain degree of credibility, but the application scenarios of such datasets are relatively limited. There is a certain gap between the recognition and the first type of datasets. In addition to using the first type of dataset, yaclin [35] also uses the competition-related dataset CAMRa2011. This dataset comes from a data science competition. This dataset contains relevant information such as users, items, groups, group size, user-item rating and group-item rating. Such datasets are often more abundant, but the application scenarios of such data sets are more special, which cannot meet the research needs of this paper. Jeong [22] discusses group recommendations on the topic of group gatherings using the group data sets within meetup, but these datasets still deviate from the topic of this paper, so we did not select such datasets. Finally, the least crowded dataset [12, 21, 27] used is a collection of data collected by the authors of the papers themselves, which are less suitable for reuse. In summary, since the latter two types of datasets have some problems, in this paper, we still choose and mainstream dataset in group recommendation experiments and use simulated group relationships to carry out our experiments.

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Wang, Z., Li, Y. Knowledge graph-based graph neural network models for multi-perspective modeling of group preferences. Electron Commer Res (2023). https://doi.org/10.1007/s10660-023-09771-9

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