Abstract
The scholarly information retrieval systems help us to access a broader range of information. However, the currently available systems do not always ensure data authenticity and real-time. To solve these problems, we propose a Scholar Think Tank System based on an actual scholar database from SCHOLAT to build a high-quality and reliable database. Our system ensures the authenticity and real-time data through data synchronization and manual information collection from scholars not registered to SCHOLAT. In addition, users may be interested in scholars based more on common research content, which reminds us that we need to pay attention to the similarity between scholars. With this inspiration, we have developed a semantic similarity-based scholar recommendation service. We use the pre-training language model SBERT to calculate the similarity between scholars through their profiles and devise an incremental update algorithm to reduce the use of computing resources. Our system has been developed and online.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bastopcu, M., Ulukus, S.: Who should google scholar update more often? In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 696–701. IEEE (2020)
Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., Specia, L.: SemEval-2017 task 1: semantic textual similarity-multilingual and cross-lingual focused evaluation. arXiv preprint arXiv:1708.00055 (2017)
Chen, Y., Ding, C., Hu, J., Chen, R., Hui, P., Fu, X.: Building and analyzing a global co-authorship network using google scholar data. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 1219–1224 (2017)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dong, L., et al.: Unified language model pre-training for natural language understanding and generation. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Elkahky, A.M., Song, Y., He, X.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web, pp. 278–288 (2015)
Fan, W., Derr, T., Ma, Y., Wang, J., Tang, J., Li, Q.: Deep adversarial social recommendation. arXiv preprint arXiv:1905.13160 (2019)
Fan, W., et al.: Graph neural networks for social recommendation. In: The World Wide Web Conference, pp. 417–426 (2019)
Fan, W., Ma, Y., Yin, D., Wang, J., Tang, J., Li, Q.: Deep social collaborative filtering. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 305–313 (2019)
Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2333–2338 (2013)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM Conference on Recommender Systems, pp. 135–142 (2010)
Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: SpanBERT: improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Linguist. 8, 64–77 (2020)
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: a lite BERT for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)
Liu, J., Ren, J., Zheng, W., Chi, L., Lee, I., Xia, F.: Web of scholars: a scholar knowledge graph. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2153–2156 (2020)
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Lopes, G.R., Moro, M.M., Wives, L.K., de Oliveira, J.P.M.: Collaboration recommendation on academic social networks. In: Trujillo, J., Dobbie, G., Kangassalo, H., Hartmann, S., Kirchberg, M., Rossi, M., Reinhartz-Berger, I., Zimányi, E., Frasincar, F. (eds.) ER 2010. LNCS, vol. 6413, pp. 190–199. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16385-2_24
May, C., Wang, A., Bordia, S., Bowman, S.R., Rudinger, R.: On measuring social biases in sentence encoders. arXiv preprint arXiv:1903.10561 (2019)
Qiao, Y., Xiong, C., Liu, Z., Liu, Z.: Understanding the behaviors of BERT in ranking. arXiv preprint arXiv:1904.07531 (2019)
Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. arXiv preprint arXiv:1908.10084 (2019)
Shen, Y., He, X., Gao, J., Deng, L., Mesnil, G.: A latent semantic model with convolutional-pooling structure for information retrieval. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 101–110 (2014)
Song, K., Tan, X., Qin, T., Lu, J., Liu, T.Y.: MASS: masked sequence to sequence pre-training for language generation. arXiv preprint arXiv:1905.02450 (2019)
Tang, F., Zhu, J., He, C., Fu, C., He, J., Tang, Y.: SCHOLAT: an innovative academic information service platform. In: Cheema, M.A., Zhang, W., Chang, L. (eds.) ADC 2016. LNCS, vol. 9877, pp. 453–456. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46922-5_38
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998 (2008)
Tang, J., Hu, X., Liu, H.: Social recommendation: a review. Soc. Netw. Anal. Min. 3, 1113–1133 (2013)
Thelwall, M., Kousha, K.: ResearchGate articles: age, discipline, audience size, and impact. J. Am. Soc. Inf. Sci. 68(2), 468–479 (2017)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Walters, W.H.: Google scholar coverage of a multidisciplinary field. Inf. Process. Manage. 43(4), 1121–1132 (2007)
Yan, W., Zhang, Y., Hu, T., Kudva, S.: How does scholarly use of academic social networking sites differ by academic discipline? A case study using researchgate. Inf. Process. Manage. 58(1), 102430 (2021)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: Generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: evaluating text generation with bert. arXiv preprint arXiv:1904.09675 (2019)
Acknowledgements
This work was supported by National Natural Science Foundation of China No. U1811263.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lan, Y., Lin, R., Mao, C. (2024). A Scholarly Information Retrieval System Incorporating Recommendation with Semantic Similarity. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_22
Download citation
DOI: https://doi.org/10.1007/978-981-99-9637-7_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9636-0
Online ISBN: 978-981-99-9637-7
eBook Packages: Computer ScienceComputer Science (R0)
