TAGAN: an academic paper adversarial recommendation algorithm incorporating fine-grained semantic features

Jinyang SUN; Baisong LIU; Hao REN; Jiangbo QIAN

doi:10.11959/j.issn.1000-0801.2021197

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TAGAN: an academic paper adversarial recommendation algorithm incorporating fine-grained semantic features

Research and Development | 更新时间：2024-06-05

- TAGAN: an academic paper adversarial recommendation algorithm incorporating fine-grained semantic features
- Telecommunications Science Vol. 37, Issue 8, Pages: 57-65(2021)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021197
  CLC： TP391.3
- Published Online：2021-08，
  
  Published：20 August 2021
- 稿件说明：
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Jinyang SUN, Baisong LIU, Hao REN, et al. TAGAN: an academic paper adversarial recommendation algorithm incorporating fine-grained semantic features[J]. Telecommunications science, 2021, 37(8): 57-65.
DOI：

Jinyang SUN, Baisong LIU, Hao REN, et al. TAGAN: an academic paper adversarial recommendation algorithm incorporating fine-grained semantic features[J]. Telecommunications science, 2021, 37(8): 57-65. DOI： 10.11959/j.issn.1000-0801.2021197.

摘要

学术论文推荐旨在为用户提供个性化的论文资源，针对协同过滤方法面临数据高度稀疏和缺乏负样本的问题，提出了一种融合细粒度语义特征的学术论文对抗推荐模型——TAGAN（title and abstract GAN）。首先，基于具有语义特征的标题和摘要，使用卷积神经网络（CNN）提取标题的全局特征，并构建一个双层的长短期记忆（LSTM）网络分别对摘要的单词序列和语句序列建模，同时，引入注意力机制将标题和摘要进行语义上的关联。然后，将论文的语义特征融入基于生成对抗网络（GAN）的推荐框架中并进行训练，其生成模型会拟合用户的兴趣偏好，能有效替代负采样过程。最后，通过在公开数据集上的实验对比，TAGAN在各个指标上都优于基线模型，验证了TAGAN的有效性。

Abstract

Academic paper recommendation aims to provide users with personalized paper resources.Collaborative filtering methods face the problems of highly sparse data and lack of negative samples.Considering the above challenges

an academic paper recommendation algorithm TAGAN（title and abstract GAN）which incorporated fine-grained semantic features was presented.Firstly

based on titles and abstracts provide abundant semantic features

convolutional neural networks (CNN) was used to extract the global features of the titles

a two-layer long and short-term memory network (LSTM) was built to model abstract words separately.At the same time

the attention mechanism was proposed to associate the title and the abstract semantically.Then

the semantic features of the paper were integrated into the recommendation framework based on generative adversarial network (GAN).The generative model will fit the user’s interest preferences and can effectively replace the negative sampling process.Finally

through the experimental comparison on the public dataset

TAGAN is better than the baseline models in all indicators

which verifies the effectiveness of TAGAN.

关键词

Keywords

references

BAI X M , WANG M Y , LEE I , et al . Scientific paper recommendation:a survey [J ] . IEEE Access , 2019 ( 7 ): 9324 - 9339 .

MA X , ZHANG Y , ZENG J F . Newly published scientific papers recommendation in heterogeneous information networks [J ] . Mobile Networks and Applications , 2019 , 24 ( 1 ): 69 - 79 .

HASSAN H A M , . Personalized research paper recommendation using deep learning [C ] // Proceedings of the 25th Conference on User Modeling,Adaptation and Personalization . New York:ACM Press , 2017 : 327 - 330 .

ZHANG S , YAO L N , SUN A X , et al . Deep learning based recommender system [J ] . ACM Computing Surveys , 2019 , 52 ( 1 ): 1 - 38 .

NACHMANI E , MARCIANO E , LUGOSCH L , et al . Deep learning methods for improved decoding of linear codes [J ] . IEEE Journal of Selected Topics in Signal Processing , 2018 , 12 ( 1 ): 119 - 131 .

QIAN K , CHOZHIYATH RAMAN P , LI Y Y , et al . PARTNER:human-in-the-loop entity name understanding with deep learning [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 9 ): 13634 - 13635 .

DAI T , ZHU L , WANG Y X , et al . Attentive stacked denoising autoencoder with Bi-LSTM for personalized context-aware citation recommendation [J ] . IEEE/ACM Transactions on Audio,Speech,and Language Processing , 2020 ( 28 ): 553 - 568 .

BERTINI M , FERRACANIi A , PAPUCCI R , et al . Keeping up with the influencers:improving user recommendation in instagram using visual content [C ] // Proceedings of User Modeling,Adaptation and Personalization (UMAP) . 2020 .

XIA H B , LI J J , LIU Y . Collaborative filtering recommendation algorithm based on attention GRU and adversarial learning [J ] . IEEE Access , 2020 ( 8 ): 208149 - 208157 .

YANG L B , ZHENG Y , CAI X Y , et al . A LSTM based model for personalized context-aware citation recommendation [J ] . IEEE Access , 2018 ( 6 ): 59618 - 59627 .

WANG H , WANG N Y , YEUNG D Y . Collaborative deep learning for recommender systems [C ] // Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Sydney NSW Australia . New York:ACM Press , 2015 : 1235 - 1244 .

HE X N , HE Z K , SONG J K , et al . NAIS:neural attentive item similarity model for recommendation [J ] . IEEE Transactions on Knowledge and Data Engineering , 2018 , 30 ( 12 ): 2354 - 2366 .

GOODFELLOW I , POUGET-ABADIE J ,, MIRZA M , et al . Generative adversarial nets [J ] . Advances in Neural Information Processing Systems , 2014 , 2672 - 2680 .

WANG J , YU L T , ZHANG W N , et al . IRGAN:a minimax game for unifying generative and discriminative information retrieval models [C ] // Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 2017 : 515 - 524 .

CHAE D K , KANG J S , KIM S W , et al . CFGAN:a generic collaborative filtering framework based on generative adversarial networks [C ] // Proceedings of the 27th ACM International Conference on Information and Knowledge Management . New York:ACM Press , 2018 : 137 - 146 .

HE X N , HE Z K , DU X Y , et al . Adversarial personalized ranking for recommendation [C ] // The 41st International ACM SIGIR Conference on Research ＆ Development in Information Retrieval . New York:ACM Press , 2018 : 355 - 364 .

KIM D , PARK C , OH J , et al . Convolutional matrix factorization for document context-aware recommendation [C ] // Proceedings of the 10th ACM Conference on Recommender Systems . New York:ACM Press , 2016 : 233 - 240 .

CREMONESI P , KOREN Y , TURRIN R . Performance of recommender algorithms on top-n recommendation tasks [C ] // Proceedings of the Fourth ACM Conference on Recommender Systems - RecSys '10 . New York:ACM Press , 2010 : 39 - 46 .

RENDLE S , FREUDENTHALER C , GANTNER Z , et al . BPR:Bayesian personalized ranking from implicit feedback [C ] // Proceedings of Uncertainty in Artificial Intelligence , 2009 : 452 - 461 .

HE X N , LIAO L Z , ZHANG H W , et al . Neural collaborative filtering [C ] // Proceedings of the 26th International Conference on World Wide Web . Republic and Canton of Geneva,Switzerland:International World Wide Web Conferences Steering Committee , 2017 : 173 - 182 .

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