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A novel meta-graph-based attention model for event recommendation

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Abstract

Due to the popular trend of combining online and offline interactions among users in event-based social networks (EBSNs), event recommendation which helps users discover their interesting events has become progressively urgent. Different from classic item recommendation, candidate events usually have short life cycle and occur in the future, resulting in severe challenges of data sparsity and cold-start. However, these problems are not well studied by previous works. In this article, we propose a Meta-Graph-based Attention Recommendation (MGAR) model to tackle aforementioned challenges by fully exploring complex semantic information based on meta-graphs extracted from EBSNs. First, we model the interactions between different entities as a heterogeneous information network and construct multiple meta-graphs to characterize the latent semantic preferences of users. Subsequently, we utilize convolutional neural networks and attention mechanisms to learn user and event latent factors by extracting semantic features of meta-graphs. Furthermore, the fused latent features are utilized to predict the ratings of a user to events and the events with top-k scores are recommended to the user. We collect several real-world datasets from a popular EBSN platform and conduct extensive experiments on the datasets. Our proposed model attains superior recommendation performance over several state-of-the-art approaches. Moreover, the results demonstrate that meta-graphs can reveal the semantic properties between users and events and improve event recommendation performance.

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Notes

  1. www.meetup.com.

  2. www.plus.google.com/events.

  3. www.beijing.douban.com.

  4. RSVP stands for the French expression “répondez s’il vous plaét,” meaning “please respond”.

  5. Events with the same group, description and location are referred to as periodic events.

  6. http://jgibblda.sourceforge.net/.

  7. http://www.nltk.org/.

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Acknowledgements

The work was supported in part by the National Key R&D Program of China 2020AAA0107100 and 2020YFB1406900, the National Science Foundation of China grants 62072365 and 61772392, the Key Research and Development Program of Shaanxi (Program No.2020KW-002), and the Innovation Capability Support Plan of Shaanxi (Program No.2021PT-010).

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Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China

    Xiaolong Jiang, Heli Sun, Bo Zhang, Liang He & Xiaolin Jia

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Appendices

Appendix A

In Table 10, we show other twelve available metagraphs for Meetup datasets.

Table 10 Other meaningful metagraphs considering for Meetup datasets
Full size table

Appendix B

To further explore the impact of the partition of datasets on performance, we generate two new kinds of datasets (trainset_4month and trainset_6month) for each of the three cities (Phoenix, Chicago and New York). For each new dataset, there are four sub-datasets with different timestamps. The length of test set for each sub-dataset remains one month, and different from the original dataset (trainset_5month), the lengths of training set for each sub-dataset in new datasets are four months and six months, respectively. The average result of sub-datasets in a new dataset is treated as the final performance of this new dataset. We evaluate the performance of our model (MGAR) and the strongest baseline (NeuACF) over the new datasets using metrics P@10, MAP, Rec@10 and NDCG@10. Table 11 shows the result of this comparison for the three cities. From Table 11, we observe that with the increase in the length of training set, the performance of both MGAR and NeuACF grows. However, the running speed reduces. One of main reasons is that there is a slight increase in the proportions of training RSVPs to all RSVPs when a training set gets larger and test set is almost constant. In addition, for MGAR and NeuACF, there is a relatively large improvement in the performance when the length of training set increases from four months to five months for all cities, while a slight improvement when it grows from five months to six months. Furthermore, compared to NeuACF, our model can always obtain appreciable increases over all metrics under any dataset. In the paper, we set the length of training set to five months by balancing performance and training time.

Table 11 Average performance on datasets of different sizes for MGAR and NeuACF
Full size table

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Jiang, X., Sun, H., Zhang, B. et al. A novel meta-graph-based attention model for event recommendation. Neural Comput & Applic 34, 14659–14682 (2022). https://doi.org/10.1007/s00521-022-07301-6

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