Abstract
Collaboration recommendation from scholarly big data is an important but challenging problem as it might suffer the difficulty of accurate recommendation from three aspects: how to efficiently integrate the available author-related information, how to precisely describe the characteristics of the scholarly data samples, and how to extract the intrinsic features that are more suitable for collaboration recommendation. Facing these challenges, we incorporate the temporal and academic-influence information of the publications with the spatial information of the researchers to present a spatial–temporal restricted supervised learning (STSL) model for collaboration recommendation. We first present a topic clustering model to determine the topic distribution vector of each researcher, where a temporal parameter is introduced to exponentially weight each topic distribution vector and an academic-influence parameter is further introduced to linearly combine all the topic distribution vectors of the publications. Then, inspired by the geographical-advantage phenomena in collaboration, spatial labels are generated by using the personal information of the researchers. Furthermore, considering that the publication data enhanced by spatial–temporal and academic-influence descriptions usually exhibit multimodal or mixmodal properties, we propose a data-driven supervised learning model to extract the intrinsic features inhered in data, which determines a low-dimensional recommendation subspace. A number of experiments are conducted to test the impact of the topic-clustering number, the temporal parameter, the academic-influence parameter, and the number of extracted features. Besides, several widely-used models are adopted to compare with the proposed STSL model for collaboration recommendation, with results verifying its feasibility and effectiveness.
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Alinani, K., Alinani, A., Narejo, D. H., & Wang, G. (2018). Aggregating author profiles from multiple publisher networks to build a list of potential collaborators. IEEE Access, 6, 20298–20308.
Araki, M., Katsurai, M., Ohmukai, I., & Takeda, H. (2017). Interdisciplinary collaborator recommendation based on research content similarity. IEICE Transactions on Information & Systems, E100.D(4), 1–8.
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711–720.
Benchettara, N., & Kanawati, R. (2010). A supervised machine learning link prediction approach for academic collaboration recommendation. In Proceedings of ACM conference on recommender systems (pp. 253–256).
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research Archive, 3, 993–1022.
Cai, Z. H., Wang, J. S., Li, Y. K., & Liu, S. B. (2017). A collaborative filtering recommendation algorithm based on difference and correlation of users’ ratings. In Proceedings of international conference of pioneering computer scientists (pp. 52–63).
Chen, J., Zhang, H., He, X., Liu, W., Liu, W., & Chua, T. S. (2017a). Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention. In Proceedings of the international conference on research and development in information retrieval (pp. 335–344).
Chen, Z., Huang, W., & Lv, Z. (2017b). Towards a face recognition method based on uncorrelated discriminant sparse preserving projection. Multimedia Tools and Applications, 76(17), 17669–17683.
Chu, K. C., & Yeh, C. C. (2016). Knowledge flow of biomedical informatics domain: Position-based co-citation analysis approach. In Proceedings of IEEE/ACM international conference on advances in social networks analysis and mining (pp. 1119–1126).
Cui, J., Wen, J., Li, Z., & Bin, L. (2015). Discriminant non-negative graph embedding for face recognition. Neurocomputing, 149, 1451–1460.
Gao, Z., Fan, Y. S., Wu, C., Tan, W., Zhang, J., Ni, Y., et al. (2018). Seco-lda: Mining service co-occurrence topics for composition recommendation. IEEE Transactions on Services Computing, 14(8), 1–14.
Gutierrez-Santos, S., Mavrikis, M., Geraniou, E., & Poulovassilis, A. (2017). Similarity-based grouping to support teachers on collaborative activities in an exploratory mathematical microworld. IEEE Transactions on Emerging Topics in Computing, 5(1), 56–68.
Kong, X., Jiang, H., Wang, W., Bekele, T. M., Xu, Z., & Wang, M. (2017). Exploring dynamic research interest and academic influence for scientific collaborator recommendation. Scientometrics, 113(1), 369–385.
Kong, X., Jiang, H., Yang, Z., Xu, Z., Feng, X., & Tolba, A. (2016). Exploiting publication contents and collaboration networks for collaborator recommendation. PLoS ONE, 11(2), e0148492.
Li, Z., Zhang, H., Wang, S., Huang, F., Li, Z., & Zhou, J. (2018). Exploit latent dirichlet allocation for collaborative filtering. Frontiers of Computer Science, 1, 1–11.
Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. Berlin: Springer.
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008). Arnetminer: Extraction and mining of academic social networks. In Proceedings of international conference on knowledge discovery and data mining (pp. 990–998).
Wang, D., Liang, Y., Xu, D., Feng, X., & Guan, R. (2018). A content-based recommender system for computer science publications. Knowledge-Based Systems, 157, 1–9.
Wei, J., He, J., Chen, K., Zhou, Y., & Tang, Z. (2017). Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69, 29–39.
West, J. D., Wesleysmith, I., & Bergstrom, C. T. (2016). A recommendation system based on hierarchical clustering of an article-level citation network. IEEE Transactions on Big Data, 2(2), 113–123.
Xia, F., Chen, Z., Wang, W., & Li, J. (2014). Mvcwalker: Random walk-based most valuable collaborators recommendation exploiting academic factors. IEEE Transactions on Emerging Topics in Computing, 2(3), 364–375.
Xu, D., Yan, S., Tao, D., Lin, S., & Zhang, H.-J. (2007). Marginal fisher analysis and its variants for human gait recognition and content-based image retrieval. IEEE Transactions on Image Processing, 16(11), 2811–2821.
Yang, X., Liang, C., Zhao, M., Wang, H., Ding, H., Liu, Y., et al. (2017). Collaborative filtering-based recommendation of online social voting. IEEE Transactions on Computational Social Systems, 4(1), 1–13.
Yerma, S., & Majhvar, A. K. (2017). Updated page rank of dynamically generated research authors’ pages: A new idea. In Proceedings of IEEE international conference on recent trends in electronics, information & communication technology (pp. 879–882).
Yuan, X., Wang, Y., Yang, C., Gui, W., & Jiang, Q. (2017). Output-related feature representation for soft sensing based on supervised locality preserving projections. In Proceedings of international symposium on advanced control of industrial processes (pp. 577–582).
Zhang, Q., & Chu, T. (2017). Learning in multimodal and mixmodal data: Locality preserving discriminant analysis with kernel and sparse representation techniques. Multimedia Tools and Applications, 76(14), 15465–15489.
Zhao, T., Zhao, H. V., & King, I. (2015). Exploiting game theoretic analysis for link recommendation in social networks. In Proceedings of the ACM international conference on information and knowledge management (pp. 851–860).
Zheng, W., Zou, C., & Zhao, L. (2005). Weighted maximum margin discriminant analysis with kernels. Neurocomputing, 67, 357–362.
Acknowledgements
This work was supported by NSFC (No. 61503375), and the Fundamental Research Funds for the Central Universities in UIBE (CXTD10-05,18QD18).
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Zhang, Q., Mao, R. & Li, R. Spatial–temporal restricted supervised learning for collaboration recommendation. Scientometrics 119, 1497–1517 (2019). https://doi.org/10.1007/s11192-019-03100-4
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DOI: https://doi.org/10.1007/s11192-019-03100-4