Abstract
With the rapid development of e-commerce, how to recommend the groups that are most likely to buy a certain kind of items to the merchants accurately has become an increasing research of scholars. However, the existing group recommendation technology rarely considers the influence of the close relationship between users on user preferences. Thus we propose a group recommendation model NGRN to generate groups and make recommendations. First we extract k-core groups on the social network, the groups meet the conditions that each user has k neighbors at least. Then we get the recommendable probability of candidate groups under different items. At the same time, the validity of this method is verified on two public datasets. Experiment shows our model has a great improvement for recommendation accuracy compared with other models.
The work is partially supported by the National Natural Science Foundation of China (Nos. U1736104), and the Fundamental Research Funds for the Central Universities (No. N171602003), Ten Thousand Talent Program (No. ZX20200035), Liaoning Distinguished Professor (No. XLYC1902057).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Amer-Yahia, S., Roy, S.B., Chawla, A., Das, G., Yu, C.: Group recommendation: semantics and efficiency. Proc. VLDB Endow. 2(1), 754–765 (2009)
Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., Leonardi, S.: Online team formation in social networks. In: Proceedings of the 21st World Wide Web Conference 2012, WWW 2012, pp. 839–848. ACM (2012)
Cao, D., He, X., Miao, L., An, Y., Yang, C., Hong, R.: Attentive group recommendation. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, pp. 645–654. ACM (2018)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018)
Ghazanfar, M.A., Prügel-Bennett, A.: The advantage of careful imputation sources in sparse data-environment of recommender systems: generating improved SVD-based recommendations. Informatica (Slovenia) 37(1), 61–92 (2013)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017, Conference Track Proceedings. OpenReview.net (2017)
LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521(7553), 436–444 (2015)
Ntoutsi, E., Stefanidis, K., Nørvåg, K., Kriegel, H.-P.: Fast group recommendations by applying user clustering. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012. LNCS, vol. 7532, pp. 126–140. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34002-4_10
Ntoutsi, E., Stefanidis, K., Rausch, K., Kriegel, H.: “Strength lies in differences": diversifying friends for recommendations through subspace clustering. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, pp. 729–738. ACM (2014)
Qin, D., Zhou, X., Chen, L., Huang, G., Zhang, Y.: Dynamic connection-based social group recommendation. IEEE Trans. Knowl. Data Eng. 32(3), 453–467 (2020)
Tang, J., Hu, X., Liu, H.: Social recommendation: a review. Soc. Netw. Anal. Min. 3(4), 1113–1133 (2013). https://doi.org/10.1007/s13278-013-0141-9
Yang, X., Wang, B., Yang, K., Liu, C., Zheng, B.: A novel representation and compression for queries on trajectories in road networks. IEEE Trans. Knowl. Data Eng. 30(4), 613–629 (2018)
Yang, X., Wang, Y., Wang, B., Wang, W.: Local filtering: improving the performance of approximate queries on string collections. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data 2015, pp. 377–392. ACM (2015)
Yu, F., Zeng, A., Gillard, S., Medo, M.: Network-based recommendation algorithms: a review. CoRR abs/1511.06252 (2015)
Yuan, Q., Cong, G., Lin, C.: COM: a generative model for group recommendation. In: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 163–172. ACM (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Pu, R., Wang, B., Song, X., Xie, X., Qin, J. (2020). A Neighbor-Aware Group Recommendation Algorithm. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-65390-3_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65389-7
Online ISBN: 978-3-030-65390-3
eBook Packages: Computer ScienceComputer Science (R0)