一种基于多任务学习的科学文献推荐算法

doi:10.16180/j.cnki.issn1007-7820.2023.04.008

Abstract

Abstract:

Traditional recommendation algorithms map text content through topic model or mean value of word vectorization. For the issue that existing methods cannot make full use of text information or ignore word order information, this study proposes a multi-task learning recommendation method for scientific literature. Based on the multi-task learning framework, an encoder is designed and a GL model is established. The GL model is trained to combine content recommendation and text metadata prediction, which improves the sparsity of traditional collaborative filtering and regularizes the collaborative filtering model. Finally, an evaluation test is carried out on public and private data sets respectively, and the superiority of the proposed method is demonstrated by comparing with the existing classical methods.

Key words: recommendation system, deep learning, neural networks, multi-task learning, collaborative filtering, gated recurrent unit, collaborative topic regression, encoder

CLC Number:

TP391.3

BAI Yingqi,PALIDAN·Tuerxun . A Scientific Literature Recommendation Method Based on Multi-Task Learning[J].Electronic Science and Technology, 2023, 36(4): 59-64.

Figures/Tables 4

References 24

[1]	Guy I, Zwerdling N, Ronen I, et al. Social media recommendation based on people and tags[C]. Geneva: Proceedings of the Thirty-third International ACM SIGIR Conference on Research and Development in Information Retrieval, 2010.
[2]	Phelan O, McCarthy K, Smyth B. Using twitter to recommend real-time topical news[C]. New York: Proceedings of the Third ACM Conference on Recommender Systems, 2009.
[3]	McAuley J, Leskovec J. Hidden factors and hidden topics: Understanding rating dimensions with review text[C]. Hong Kong: Proceedings of the Seventh ACM Conference on Recommender Systems, 2013.
[4]	Chong W, Blei D M. Collaborative topic modeling for recommending scientific articles[C]. San Diego: Proceedings of the Seventeenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
[5]	Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8):30-37.
[6]	Dai A M, Le Q V. Semi-supervised sequence learning[C]. Cambridge: Proceedings of the Twenty-eighth International Conference on Neural Information Processing Systems, 2015.
[7]	Schein A I, Popescul A, Ungar L H, et al. Methods and metrics for cold-start recommendations[C]. Tampere: Proceedings of the Twenty-fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002.
[8]	Wang H, Wang N Y, Yeung D Y. Collaborative deep learning for recommender systems[C]. Sydney: Proceedings of the Twenty-first ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015.
[9]	Melville P, Mooney R J, Nagarajan R. Content-boosted collaborative filtering for improved recommendations[C]. Edmonton: Proceedings of the Eighteenth National Conference on Artificial Intelligence, 2002.
[10]	Agarwal D, Chen B C. Regression-based latent factor models[C]. Paris: Proceedings of the Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009.
[11]	Singh A P, Gordon G J. Relational learning via collective matrix factorization[C]. Las Vegas: Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.
[12]	Ma H, Yang H, Lyu M R, et al. SoRec:Social recommendation using probabilistic matrix factorization[C]. Napa Valley: Proceedings of the Seventeenth ACM Conference on Information and Knowledge Management, 2008.
[13]	Almahairi A, Kastner K, Cho K, et al. Learning distributed representations from reviews for collaborative filtering[C]. Vienna: Proceedings of the Ninth ACM Conference on Recommender Systems, 2015.
[14]	Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality[C]. New York: Proceedings of the Twenty-sixth International Conference on Neural Information Processing Systems, 2013.
[15]	彭慧洁. 融合多维信息的协同过滤算法研究[J]. 电子科技, 2017, 30(2):42-44.
	Peng Huijie. The research on collaborative filtering algorithm of multi-dimensional information[J]. Electronic Science and Technology, 2017, 30(2):42-44.
[16]	Bell R M, Koren Y. Lessons from the netflix prize challenge[J]. ACM SIGKDD Explorations Newsletter, 2007, 9(2):75-79. doi: 10.1145/1345448.1345465
[17]	Krestel R, Fankhauser P, Nejdl W. Latent dirichlet allocation for tag recommendation[C]. New York: Proceedings of the Third ACM Conference on Recommender Systems, 2009.
[18]	Sedhain S, Menon A K, Sanner S, et al. Autorec: Autoencoders meet collaborative filtering[C]. Florence: Proceedings of the Twenty-fourth International Conference on World Wide Web, 2015.
[19]	He H, McAuley J. VBPR: Visual bayesian personalized ranking from implicit feedback[C]. Phoenix: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016.
[20]	Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 1994, 5(2):157-166. doi: 10.1109/72.279181 pmid: 18267787
[21]	Cho K, Merrienboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]. Doha: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2014.
[22]	Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780. doi: 10.1162/neco.1997.9.8.1735 pmid: 9377276
[23]	Li J, Chen X L, Hovy E, et al. Visualizing and understanding neural models in NLP[C]. San Diego:Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016.
[24]	Weston J, Bengio S, Usunier N. Wsabie: Scaling up to large vocabulary image annotation[C]. Barcelona: Proceedings of the Twenty-second International Joint Conference on Artificial Intelligence, 2011.

A Scientific Literature Recommendation Method Based on Multi-Task Learning

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 4

References 24

Related Articles 15

Metrics

Comments

Recommended 10

[1]	CUI Zhuodong,CHEN Wei,YIN Zhong. Helmet Wearing Detection Based on Enhanced Feature Fusion Network [J]. Electronic Science and Technology, 2023, 36(4): 44-51.
[2]	TANG Zheng,ZHANG Huilin,MA Lixin,LIU Jinzhi,WANG Hao. Identification of Foreign Objects on Transmission Lines Using Lightweight Network Algorithm [J]. Electronic Science and Technology, 2023, 36(4): 71-77.
[3]	SUN Hong,LU Meike. Hybrid Recommendation Algorithm Fused with User Behavior Sequence Prediction [J]. Electronic Science and Technology, 2023, 36(4): 84-89.
[4]	LUO Ruipeng,FENG Mingke,HUANG Xin,ZOU Renling,LI Dan. A Review of Research on EEG Signal Preprocessing Methods [J]. Electronic Science and Technology, 2023, 36(4): 36-43.
[5]	LU Dongxiang. Research Progress of Node Assignment Optimization Strategy in Road Traffic Network [J]. Electronic Science and Technology, 2023, 36(3): 81-86.
[6]	ZHAO Wenjun,ZHAI Han,ZHANG Hongyan. Total Variation and Sparsity Regularized Deep Nonnegative Matrix Factorization for Hyperspectral Unmixing [J]. Electronic Science and Technology, 2023, 36(2): 53-60.
[7]	YU Qiongfang,NIU Dongyang. Mixed Prediction of Mine Pressure Time and Space Based on LSTM Network [J]. Electronic Science and Technology, 2023, 36(2): 67-72.
[8]	ZUO Bin,LI Feifei. An Effective Segmentation Method for COVID-19 CT Image Based on Attention Mechanism and Inf-Net [J]. Electronic Science and Technology, 2023, 36(2): 22-28.
[9]	WU Tong,YU Lianzhi. The Recommendation Algorithm of Extreme Deep Factorization Machine Merged with Attention Network [J]. Electronic Science and Technology, 2023, 36(1): 38-43.
[10]	BI Jiazhen,SHEN Tuo,ZHANG Xuanxiong. A Research on Distance Measurement Between Trains in Rail Transit Based on Machine Vision [J]. Electronic Science and Technology, 2022, 35(9): 37-43.
[11]	ZHANG Qiaomu,ZHONG Qianwen,SUN Ming,LUO Wencheng,CHAI Xiaodong. Research on Dynamic Monitoring Method of Pantograph-Net Contact Position in Complex Environment [J]. Electronic Science and Technology, 2022, 35(8): 66-72.
[12]	ZHAO Xuan,ZHOU Fan,YU Hancheng. Improved YOLOv3 Model Based on New Feature Extraction and Fusion Module [J]. Electronic Science and Technology, 2022, 35(7): 40-45.
[13]	SUN Kang,XUAN Xuyang,LIU Penghui,ZHAO Laijun,LONG Jie. Partial Discharge Pattern Recognition of Cable Based on CNN-DCGAN under Small Data [J]. Electronic Science and Technology, 2022, 35(7): 7-13.
[14]	Chaowei LIN,Feifei LI,Qiu CHEN. Globaland Local Scene Representation Method Based on Deep Convolutional Features [J]. Electronic Science and Technology, 2022, 35(4): 20-27.
[15]	Peng CHEN,Zilong LIU. Arrhythmia Recognition Based on GAN-CNN [J]. Electronic Science and Technology, 2022, 35(3): 45-50.