Abstract
The problem of user representation has received considerable attention in recent years. A variety of social networks include not only network structures (friendships) but also information about users’ attributes. Previous studies have explored the integration of the two information to encode users. However, these methods focus on how to fuse the target user’s friendships as a whole with its attribute information to get its representation vector, without considering the inside information of friendships, that is the influence of intimacy difference between the target user and its each friend on its representation vector. In addition, most of the above methods are supervised, which can only be applied to limited social networks analysis tasks. In this paper, we investigate a novel unsupervised method for learning the user representation by considering the influence of intimacy difference. The proposed methods take both the users’ attributes and their friendships into consideration with attribute-friendship attention network. Experimental results demonstrate that the user vectors generated by the proposed methods significantly outperform state-of-the-art user representation methods on two different scale real-world networks.
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Benton, A., Arora, R., Dredze, M.: Learning multiview embeddings of Twitter users. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 14–19 (2016)
Chen, L., Qian, T., Zhu, P., You, Z.: Learning user embedding representation for gender prediction. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 263–269. IEEE (2016)
Lefebvre-Brossard, A., Spaeth, A., Desmarais, M.C.: Encoding user as more than the sum of their parts: recurrent neural networks and word embedding for people-to-people recommendation. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 298–302. ACM (2017)
Liu, H., Wu, L., Zhang, D., Jian, M., Zhang, X.: Multi-perspective User2Vec: exploiting re-pin activity for user representation learning in content curation social network. Signal Process. 142, 450–456 (2018)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Song, Y., Lee, C.J.: Learning user embeddings from emails. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. vol. 2, pp. 733–738 (2017)
Tang, L., Liu, E.Y.: Joint user-entity representation learning for event recommendation in social network. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 271–280. IEEE (2017)
Yu, J., Gao, M., Song, Y., Fang, Q., Rong, W., Xiong, Q.: Integrating user embedding and collaborative filtering for social recommendations. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds.) CollaborateCom 2017. LNICST, vol. 252, pp. 470–479. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00916-8_44
Yu, Y., Wan, X., Zhou, X.: User embedding for scholarly microblog recommendation. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 449–453 (2016)
Zeng, Z., Yin, Y., Song, Y., Zhang, M.: Socialized word embeddings. In: IJCAI, pp. 3915–3921 (2017)
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Zhou, Y., Shang, Y., Cao, Y., Liu, Y., Tan, J. (2019). UAFA: Unsupervised Attribute-Friendship Attention Framework for User Representation. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_14
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DOI: https://doi.org/10.1007/978-3-030-29551-6_14
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