research-article

Hierarchical attention model for personalized tag recommendation

Authors:

Yuanchun Jiang,

Le WuAuthors Info & Claims

Journal of the Association for Information Science and Technology, Volume 72, Issue 2

Pages 173 - 189

https://doi.org/10.1002/asi.24400

Published: 18 January 2021 Publication History

Abstract

With the development of Web‐based social networks, many personalized tag recommendation approaches based on multi‐information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user‐item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real‐world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.

References

[1]

Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. Computer Science. In International conference on learning representations.

[2]

Belém, F. M., Almeida, J. M., & Gonçalves, M. A. (2017). A survey on tag recommendation methods. Journal of the Association for Information Science and Technology, 68(4), 830–844. https://doi.org/10.1002/asi.23736

Digital Library

[3]

Belém, F. M., Heringer, A. G., Almeida, J. M., & Gonçalves, M. A. (2019). Exploiting syntactic and neighbourhood attributes to address cold start in tag recommendation. Information Processing & Management, 56(3), 771–790. https://doi.org/10.1016/j.ipm.2018.12.009

[4]

Belém, F. M., Martins, E. F., Almeida, J. M., & Gonçalves, M. A. (2014). Personalized and object‐centered tag recommendation methods for web 2.0 applications. Information Processing & Management, 50(4), 524–553. https://doi.org/10.1016/j.ipm.2014.03.002

[5]

Fang, X., Pan, R., Cao, G., He, X., & Dai, W. (2015). Personalized tag recommendation through nonlinear tensor factorization using gaussian kernel. Paper presented at Twenty‐ninth AAAI conference on artificial intelligence.

[6]

Feng, S., Zhang, L., Wang, D., & Zhang, Y. (2014). A unified microblog user similarity model for online friend recommendation. In Natural language processing and Chinese computing (pp. 286–298). Berlin, Heidelberg: Springer.

[7]

Figueiredo, F., Pinto, H., BeléM, F., Almeida, J., GonçAlves, M., Fernandes, D., & Moura, E. (2013). Assessing the quality of textual features in social media. Information Processing & Management, 49(1), 222–247. https://doi.org/10.1016/j.ipm.2012.03.003

Digital Library

[8]

Gong, Y., & Zhang, Q. (2016). Hashtag recommendation using attention‐based convolutional neural network. In IJCAI (pp. 2782–2788).

[9]

Guo, H., Su, S., & Sun, Z. (2017). Image tag recommendation based on friendships. Multimedia Tools and Applications, 76(13), 14581–14597.

[10]

Hassan, H. A. M., Sansonetti, G., Gasparetti, F., & Micarelli, A. (2018). Semantic‐based tag recommendation in scientific bookmarking systems. Paper presented at Proceedings of the 12th ACM Conference on Recommender Systems (pp. 465–469). ACM.

[11]

Huang, S., Zhang, J., Wang, L., & Hua, X. S. (2015). Social friend recommendation based on multiple network correlation. IEEE Transactions on Multimedia, 18(2), 287–299.

Digital Library

[12]

Jäschke, R., Marinho, L., Hotho, A., Schmidt‐Thieme, L., & Stumme, G. (2007). Tag recommendations in folksonomies. In European conference on principles of data mining and knowledge discovery (pp. 506–514). Berlin, Heidelberg: Springer.

[13]

Kataria, S., & Agarwal, A. (2015). Distributed representations for content‐based and personalized tag recommendation. Paper presented at 2015 IEEE International Conference on Data Mining Workshop (ICDMW) (pp. 1388–1395). IEEE.

[14]

Krestel, R., & Fankhauser, P. (2012). Personalized topic‐based tag recommendation. Neurocomputing, 76(1), 61–70. https://doi.org/10.1016/j.neucom.2011.04.034

Digital Library

[15]

Krestel, R., Fankhauser, P., & Nejdl, W. (2009). Latent dirichlet allocation for tag recommendation. Paper presented at Proceedings of the third ACM conference on recommender systems (pp. 61–68). ACM.

[16]

Li, J., Xu, H., He, X., Deng, J., & Sun, X. (2016). Tweet modeling with LSTM recurrent neural networks for hashtag recommendation Paper presented at. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 1570–1577). IEEE.

[17]

Li, X., Guo, L., & Zhao, Y. E. (2008). Tag‐based social interest discovery. Paper presented at Proceedings of the 17th International Conference on World Wide Web (pp. 675–684). ACM.

[18]

Li, Y., Liu, T., Jiang, J., & Zhang, L. (2016). Hashtag recommendation with topical attention‐based LSTM. Paper presented at Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers (pp. 3019–3029).

[19]

Lipczak, M., Hu, Y., Kollet, Y., & Milios, E. (2009). Tag sources for recommendation in collaborative tagging systems. ECML PKDD Discovery Challenge, 497, 157–172.

[20]

Lipczak, M., & Milios, E. (2011). Efficient tag recommendation for real‐life data. ACM Transactions on Intelligent Systems and Technology (TIST), 3(1), 2.

[21]

Maity, S. K., Panigrahi, A., Ghosh, S., Banerjee, A., Goyal, P., & Mukherjee, A. (2019). DeepTagRec: A content‐cum‐user based tag recommendation framework for stack overflow. Paper presented at European Conference on Information Retrieval (pp. 125–131). Springer, Cham.

[22]

Martins, E. F., Belém, F. M., Almeida, J. M., & Gonçalves, M. A. (2016). On cold start for associative tag recommendation. Journal of the Association for Information Science and Technology, 67(1), 83–105.

Digital Library

[23]

Masood, M. A., Abbasi, R. A., Maqbool, O., Mushtaq, M., Aljohani, N. R., Daud, A., … Alowibdi, J. S. (2017). MFS‐LDA: A multi‐feature space tag recommendation model for cold start problem. Program, 51(3), 218–234. https://doi.org/10.1108/PROG-01-2017-0002

[24]

Menezes, G. V., Almeida, J. M., Belém, F., Gonçalves, M. A., Lacerda, A., De Moura, E. S., …, Ziviani, N. (2010). Demand‐driven tag recommendation. Paper presented at Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 402–417). Springer, Berlin, Heidelberg.

[25]

Nguyen, H. T., Wistuba, M., & Schmidt‐Thieme, L. (2017). Personalized tag recommendation for images using deep transfer learning. Paper presented at Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 705–720). Springer, Cham.

[26]

Rendle, S., Balby Marinho, L., Nanopoulos, A., & Schmidt‐Thieme, L. (2009). Learning optimal ranking with tensor factorization for tag recommendation. Paper presented at Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 727–736). ACM.

[27]

Rendle, S., & Schmidt‐Thieme, L. (2010). Pairwise interaction tensor factorization for personalized tag recommendation. Paper presented at Proceedings of the Third ACM International Conference on web Search and Data Mining (pp. 81–90). ACM.

[28]

Rush, A. M., Chopra, S., & Weston, J. (2015). A neural attention model for abstractive sentence summarization. Computer Science. Paper presented at the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 379–389). Association for Computational Linguistics, Lisbon, Portugal.

[29]

Shi, M., Liu, J., Zhou, D., Tang, M., Xie, F., & Zhang, T. (2016). A probabilistic topic model for mashup tag recommendation. Paper presented at 2016 IEEE International Conference on Web Services (ICWS) (pp. 444–451). IEEE.

[30]

Shi, M., Liu, J., Zhou, D., & Tang, Y. (2018). A topic‐sensitive method for mashup tag recommendation utilizing multi‐relational service data. IEEE Transactions on Services Computing, (Early Access). 1. https://doi.org/10.1109/TSC.2018.2805826

[31]

Sigurbjörnsson, B., & Van Zwol, R. (2008). Flickr tag recommendation based on collective knowledge. Paper presented at Proceedings of the 17th International Conference on World Wide Web (pp. 327–336). ACM.

[32]

Sun, B., Zhu, Y., Xiao, Y., Xiao, R., & Wei, Y. (2018). Automatic question tagging with deep neural networks. IEEE Transactions on Learning Technologies, 12(1), 29–43. https://doi.org/10.1109/TLT.2018.2808187

[33]

Symeonidis, P., Nanopoulos, A., & Manolopoulos, Y. (2008). Tag recommendations based on tensor dimensionality reduction. Paper presented at Proceedings of the 2008 ACM Conference on Recommender Systems (pp. 43–50). ACM.

[34]

Wang, H., Chen, B., & Li, W. J. (2013). Collaborative topic regression with social regularization for tag recommendation. Paper presented at Twenty‐Third International Joint Conference on Artificial Intelligence.

[35]

Wang, J., Hong, L., & Davison, B. D. (2009). Tag recommendation using keywords and association rules. Paper presented at Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Bled, Slovenia.

[36]

Wu, Y., Xi, S., Yao, Y., Xu, F., Tong, H., & Lu, J. (2018). Guiding supervised topic modeling for content based tag recommendation. Neurocomputing, 314, 479–489. https://doi.org/10.1016/j.neucom.2018.07.011

[37]

Wu, Y., Yao, Y., Xu, F., Tong, H., & Lu, J. (2016). Tag2word: Using tags to generate words for content based tag recommendation. Paper presented at Proceedings of the 25th ACM international on conference on information and knowledge management (pp. 2287–2292). ACM.

[38]

Xu, C., & Zhang, Q. (2019). The dominant factor of social tags for users' decision behavior on e‐commerce websites: Color or text. Journal of the Association for Information Science and Technology, 70(9), 942–953.

Digital Library

[39]

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., …, Bengio, Y. (2015). Show, attend and tell: Neural image caption generation with visual attention. Paper presented at The 32nd International Conference on Machine Learning (pp. 2048–2057).

[40]

Yang, P., Song, Y., & Ji, Y. (2015). Tag‐based user interest discovery though keywords extraction in social network. Paper presented at International Conference on Big Data Computing and Communications (pp. 363–372). Springer, Cham.

[41]

Yang, Y., Han, L., Gou, Z., Duan, B., Zhu, J., & Yan, H. (2018). Tagrec‐CMTF: Coupled matrix and tensor factorization for tag recommendation. IEEE Access, 6, 64142–64152. https://doi.org/10.1109/ACCESS.2018.2877764

[42]

Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical attention networks for document classification. Paper presented at Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1480–1489).

[43]

Zhang, Q., Wang, J., Huang, H., Huang, X., & Gong, Y. (2017). Hashtag recommendation for multimodal microblog using co‐attention network. Paper presented at IJCAI (pp. 3420–3426).

[44]

Zhang, S., Yao, Y., Xu, F., Tong, H., Yan, X., & Lu, J. (2019). Hashtag recommendation for photo sharing services. Paper presented at Association for the Advancement of Artificial Intelligence, 33, 5805, 5812.

[45]

Zhao, W., Guan, Z., & Liu, Z. (2015). Ranking on heterogeneous manifolds for tag recommendation in social tagging services. Neurocomputing, 148, 521–534. https://doi.org/10.1016/j.neucom.2014.07.011

Cited By

Che SLiu HLiu S(2024)Tagging Items with Emerging Tags: A Neural Topic Model Based Few-Shot Learning ApproachACM Transactions on Information Systems10.1145/364185942:4(1-37)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1145/3641859
Chen XZhang JWang LDai QDong ZTang RZhang RChen LZhao WWen JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)REASONERProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666760(14497-14515)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666760
Wang YGe SZhao XWu XXu TMa CZheng ZSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Doctor Specific Tag Recommendation for Online Medical Record ManagementProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599810(5150-5161)Online publication date: 6-Aug-2023
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Hierarchical attention model for personalized tag recommendation

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Published In

cover image Journal of the Association for Information Science and Technology

Journal of the Association for Information Science and Technology Volume 72, Issue 2

February 2021

130 pages

ISSN:2330-1635

EISSN:2330-1643

DOI:10.1002/asi.v72.2

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© 2020 Association for Information Science and Technology.

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John Wiley & Sons, Inc.

United States

Publication History

Published: 18 January 2021

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Cited By

Che SLiu HLiu S(2024)Tagging Items with Emerging Tags: A Neural Topic Model Based Few-Shot Learning ApproachACM Transactions on Information Systems10.1145/364185942:4(1-37)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1145/3641859
Chen XZhang JWang LDai QDong ZTang RZhang RChen LZhao WWen JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)REASONERProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666760(14497-14515)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666760
Wang YGe SZhao XWu XXu TMa CZheng ZSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Doctor Specific Tag Recommendation for Online Medical Record ManagementProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599810(5150-5161)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599810
Yi JRen XChen Z(2023)Multi-auxiliary Augmented Collaborative Variational Auto-encoder for Tag RecommendationACM Transactions on Information Systems10.1145/357893241:4(1-25)Online publication date: 8-Apr-2023
https://dl.acm.org/doi/10.1145/3578932
Fei ZWang JLiu KAttahi EHuang B(2023)Deep feature fusion‐based stacked denoising autoencoder for tag recommendation systemsIET Cyber-Systems and Robotics10.1049/csy2.120955:3Online publication date: 26-Aug-2023
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Zhang YWu MZhang GLu J(2023)Stepping beyond your comfort zoneJournal of the Association for Information Science and Technology10.1002/asi.2475474:7(775-790)Online publication date: 5-Jun-2023
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Vagliano IGalke LScherp A(2022)Recommendations for item set completion: on the semantics of item co-occurrence with data sparsity, input size, and input modalitiesInformation Retrieval10.1007/s10791-022-09408-925:3(269-305)Online publication date: 1-Sep-2022
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