Abstract
In the case of information overload of academic papers, the demand for academic paper recommendation is increasing. Most of the existing paper recommendation methods only utilize scholar friendship or paper content information, and ignore the influence of temporal weight on research interest, and hence they are hard to obtain good recommendation quality. Aiming at this problem, the method HNTA for academic paper recommendation based on the combination of heterogeneous network and temporal attributes is proposed. HNTA firstly constructs a heterogeneous network composed of different types of entities to calculate the similarity between two papers, and then the temporal attribute is introduced into scholars’ research interests which are divided into instant interests and continuous interests to calculate the similarity between scholars and papers. Finally, by weighting the above two similarities, the purpose of recommending papers to scholars is achieved. Overall, HNTA can not only comprehensively utilize both relationships of scholars and the content information of papers, but also it considers the impact of the temporal weight of scholars' research interests. By conducting comparative experiments on the data set of the real academic social network: SCHOLAT, the results show that HNTA performs better than traditional paper recommendation methods.
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Chun-Hua, T., Peter, B.: Exploring social recommendations with visual diversity-promoting interfaces. ACM Trans. Interact. Intell. Syst. 10(1), 5:1–5:34 (2020)
Venugopal, K., Srikantaiah, K., Nimbhorkar, S.: Web Recommendations Systems, 1st edn. Springer, Heidelberg (2020). https://doi.org/10.1007/978-981-15-2513-1
Yiu-Kai, N.: CBRec: a book recommendation system for children using the matrix factorisation and content-based filtering approaches. IJBIDM 16(2), 129–149 (2020)
Dimosthenis, B., Christos, T.: Promoting diversity in content based recommendation using feature weighting and LSH. J. Artif. Intell. Appl. Innov. 16(1), 452–461 (2020)
Braja, G., Vahed, M., Babak, S., Nan, D., et al.: A content-based literature recommendation system for datasets to improve data reusability - A case study on Gene Expression Omnibus (GEO) datasets. J. Biomed. Informat. 104, 103399 (2020)
Kadyanan, I., Dwidasmara, I., Mahendra, I.: A hybrid collaborative filtering recommendation algorithm: integrating content information and matrix factorisation. IJGUC 11(3), 367–377 (2020)
Shunmei, M., Qianmu, L., Jing, Z., et al.: Temporal-aware and sparsity-tolerant hybrid collaborative recommendation method with privacy preservation. Concurr. Comput. Pract. Exp. 32(2), 5447 (2020)
Depeng, D., Chuangxia, C., Wenhui, Y., et al.: A semantic-aware collaborative filtering recommendation method for emergency plans in response to meteorological hazards. Intell. Data Anal. 24(3), 705–721 (2020)
Antonio, G., Maxim, N., Dheevatsa, M., et al.: Mixed dimension embeddings with application to memory-efficient recommendation systems. CoRR abs/1909.11810 (2019)
Kaya, B.: Hotel recommendation system by bipartite networks and link prediction. Inf. Sci. 46(1), 53–63 (2019)
Liulan, Z., Jing, L., Weike, P., et al.: Sequence-aware factored mixed similarity model for next-item recommendation. In: BigComp, pp. 181–188 (2020)
Hanwen, L., Huaizhen, K., Chao, Y., et al.: Keywords-driven and popularity-aware paper recommendation based on undirected paper citation graph. Complexity 2020, 2085638:1–2085638:15 (2020)
Hashem, P., Parham, M., Shahrokh, E.: TCFACO: trust-aware collaborative filtering method based on ant colony optimization. Expert Syst. Appl. 18, 152–168 (2019)
Manju, G., Abhinaya, P., Hemalatha, M.R., et al.: Cold start problem alleviation in a research paper recommendation system using the random walk approach on a heterogeneous user-paper graph. IJIIT 16(2), 24–48 (2020)
Pan, L., Dai, X., Huang, S., Chen, J.: Academic paper recommendation based on heterogeneous graph. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds.) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. LNCS (LNAI), vol. 9427, pp. 381–392. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25816-4_31
Xiangfu, M., Longbing, C., Xiaoyan, Z., et al.: Top-k coupled keyword recommendation for relational keyword queries. Knowl. Inf. Syst. 50(3), 883–916 (2017). https://doi.org/10.1007/s10115-016-0959-3
Rose, C., Kathryn, M., Maxine, E., et al.: Explainable entity-based recommendations with knowledge graphs. CoRR. abs/1707.05254 (2017)
Guibing, G., Bowei, C., Xiaoyan, Z., et al.: Leveraging title-abstract attentive semantics for paper recommendation. In: AAAI, pp. 67–74 (2020)
Feng, Y., Hangru, W., Xinyue,Z., et al.: Study of listwise learning-to-rank recommendation method based on heterogeneous network analysis in scientific social networks. Comput. Appl. Res. 1–7 (2020)
Yongbin, Q., Yujie, S., Xiao, W.: Interest mining method of weibo users based on text clustering and interest attenuation. Comput. Appl. Res. 36(05), 1469–1473 (2019)
Guojia, W., Bo, D., Shirui, Pa., et al.: Reinforcement learning based meta-path discovery in large-scale heterogeneous information networks. In: AAAI, pp. 6094–6101 (2020)
Wang, J., Xu, W., Yan, W., et al.: Text similarity calculation method based on hybrid model of LDA and TF-IDF. In: Computer Science and Artificial Intelligence, pp. 1–8 (2019)
Gonen, S., Roee, A., Irad, B.: A weighted information-gain measure for ordinal classification trees. Expert Syst. Appl. 152, 113375 (2020)
Martin, G.: word2vec, node2vec, graph2vec, X2vec: towards a theory of vector embeddings of structured data. In: PODS, pp. 1–16 (2020)
Lantian, G., Xiaoyan, C., Fei, H., et al.: Exploiting fine-grained co-authorship for personalized citation recommendation. J. IEEE Access 5, 12714–12725 (2017)
Kazunari, S., Min-Yen, K.: Scholarly paper recommendation via user's recent research interests. J. ACM. 29–38 (2010)
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Li, W., Chang, C., He, C., Wu, Z., Guo, J., Peng, B. (2021). Academic Paper Recommendation Method Combining Heterogeneous Network and Temporal Attributes. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_33
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