[Objective] This study uses heterogeneous information network and author preference to improve the performance of scientific literature recommendation. [Methods] We proposed a new method using various semantic information. Firstly, we weighted the meta path in the heterogeneous information network of the scientific literature with the help of the author preference. Secondly, we used the DPRel algorithm to calculate the correlation between the author and the literature. Finally, we constructed the weighted author-literature matrix, and retrieved the recommendation list based on the descending order of the correlation. [Results] We examined our model with data sets from the Web of Science. Compared with the methods of single meta path, the average successful recommendation rate of the new algorithm was 6%, 8% and 6% higher in three datasets. The improvement rate of successful recommendation was 14.8%, 27.6% and 13.0%, respectively. [Limitations] In data preprocessing stage, the keywords were unified manually, which is unrealistic for massive data sets. [Conclusions] The proposed method could effectively improve the quality of scientific literature recommendation.
王勤洁, 秦春秀, 马续补, 刘怀亮, 徐存真. 基于作者偏好和异构信息网络的科技文献推荐方法研究*[J]. 数据分析与知识发现, 2021, 5(8): 54-64.
Wang Qinjie, Qin Chunxiu, Ma Xubu, Liu Huailiang, Xu Cunzhen. Recommending Scientific Literature Based on Author Preference and Heterogeneous Information Network. Data Analysis and Knowledge Discovery, 2021, 5(8): 54-64.
( Liu Jiaqi, Wang Quanmin. College Personalized Book Recommendation System Based on Improved User Collaborative Filtering Algorithm[J]. Computer & Digital Engineering, 2020, 48(10):2458-2461, 2479.)
Pan L L, Dai X Y, Huang S J, et al. Academic Paper Recommendation Based on Heterogeneous Graph[C]// Proceedings of the 14th China National Conference, CCL 2015 and 3rd International Symposium, NLP-NABD 2015. 2015:381-392.
( Wu Liaoyuan, Jiang Jun, Wang Gang. Study of Scientific Paper Recommendation Method Based on Unified Probabilistic Matrix Factorization in Scientific Social Networks[J]. Computer Science, 2016, 43(9):213-217.)
张力. 科技论文推荐算法研究[D]. 北京: 北京邮电大学, 2017.
( Zhang Li. Research on Recommendation Algorithm of Scientific Papers[D]. Beijing: Beijing University of Posts and Telecommunications, 2017.)
( Zhang Qi, Zhang Yinghua. Research on an Approach of Context Aware Collaborative Recommend for Scientific & Technical Literatures[J]. New Technology of Library and Information Service, 2012(2):10-17.)
( Sun Yizhou, Han Jiawei. Mining Heterogeneous Information Networks: Principles and Methodologies[M]. Translated by Duan Lei, Zhu Min, Tang Changjie. Beijing, China Machine Press, 2016.)
Suo X T, Wei F, Yu K. Entity Recommendation via Integrating Multiple Types of Implicit Feedback in Heterogeneous Information Network[C]// Proceedings of 2017 IEEE International Conference on Data Mining Workshops. 2017: 781-786.
( Wang Yonggui, Mei Xuanwei. Fuzzy Recommendation Algorithm for Asymmetric Heterogeneous Information Networks[J]. Computer Engineering and Applications, 2020, 56(23):74-79.)
Vahedian F, Burke R, Mobasher B. Weighted Random Walks for Meta-Path Expansion in Heterogeneous Networks[C]// Proceedings of the 10th ACM Conference on Recommender Systems. 2016:15-19.
Zhang M X, Wang J H, Wang W. HeteRank: A General Similarity Measure in Heterogeneous Information Networks by Integrating Multi-type Relationships[J]. Information Sciences, 2018, 453:389-407.
Gupta M, Kumar P. Recommendation Generation Using Personalized Weight of Meta-paths in Heterogeneous Information Networks[J]. European Journal of Operational Research, 2020, 284(2):660-674.
( Zhang Haixia, Lv Zhen, Zhang Chuanting, et al. An Improved Collaborative Filtering Recommendation Algorithm with Weighted Heterogeneous Information[J]. Journal of University of Electronic Science and Technology of China, 2018, 47(1):112-116, 152.)
Shi C, Li Y T, Zhang J W, et al. A Survey of Heterogeneous Information Network Analysis[J]. IEEE Transactions on Knowledge & Data Engineering, 2017, 29(1):17-37.
Sun Y Z, Han J W. Meta-Path-Based Search and Mining in Heterogeneous Information Networks[J]. Tsinghua Science and Technology, 2013, 18(4):329-338.
Gupta M, Kumar P, Bhasker B. HeteClass: A Meta-path Based Framework for Transductive Classification of Objects in Heterogeneous Information Networks[J]. Expert Systems with Applications, 2017, 68:106-122.
Sun Y Z, Han J W, Yan X F, et al. PathSim: Meta Path-based Top-K Similarity Search in Heterogeneous Information Networks[J]. Proceedings of the VLDB Endowment, 2011, 4(11):992-1003.
Christakis N A, Fowler J H. Social Contagion Theory:Examining Dynamic Social Networks and Human Behavior[J]. Statistics in Medicine, 2013, 32(4):556-577.