基于自编码器与多模态数据融合的视频推荐方法

顾秋阳; 琚春华; 吴功兴

doi:10.11959/j.issn.1000-0801.2021031

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基于自编码器与多模态数据融合的视频推荐方法

研究与开发 | 更新时间：2024-06-05

- 基于自编码器与多模态数据融合的视频推荐方法
- Fusion of auto encoders and multi-modal data based video recommendation method
- 电信科学 2021年37卷第2期页码：82-98
- 作者机构：
  
  1. 浙江工业大学管理学院，浙江杭州 310023
  2. 浙江工商大学，浙江杭州 310018
- 作者简介：
  
  [ "顾秋阳（1995- ），男，浙江工商大学博士生，主要研究方向为智能信息处理、数据挖掘、电子商务与物流优化等。" ]
  [ "琚春华（1962- ），男，博士，浙江工商大学教授、博士生导师，主要研究方向为智能信息处理、数据挖掘、电子商务与物流优化等。" ]
  [ "吴功兴（1974- ），男，博士，浙江工商大学副教授，主要研究方向为智能信息处理、数据挖掘、电子商务与物流优化等。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(71571162);浙江省社会科学规划重点课题项目;The Social Science Planning Key Project of Zhejiang Province(20NDJC10Z);国家社会科学基金应急管理体系建设研究专项;The National Social Science Fund Emergency Management System Construction Research Projec(20VYJ073);浙江省哲学社会科学重大课题项目;Zhejiang Philosophy and Social Science Major Project(20YSXK02ZD)
- DOI：10.11959/j.issn.1000-0801.2021031
  中图分类号： TP391
- 网络出版日期：2021-02，
  
  纸质出版日期：2021-02-20
- 稿件说明：
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顾秋阳, 琚春华, 吴功兴. 基于自编码器与多模态数据融合的视频推荐方法[J]. 电信科学, 2021,37(2):82-98.

Qiuyang GU, Chunhua JU, Gongxing WU. Fusion of auto encoders and multi-modal data based video recommendation method[J]. Telecommunications science, 2021, 37(2): 82-98.
顾秋阳, 琚春华, 吴功兴. 基于自编码器与多模态数据融合的视频推荐方法[J]. 电信科学, 2021,37(2):82-98. DOI： 10.11959/j.issn.1000-0801.2021031.

Qiuyang GU, Chunhua JU, Gongxing WU. Fusion of auto encoders and multi-modal data based video recommendation method[J]. Telecommunications science, 2021, 37(2): 82-98. DOI： 10.11959/j.issn.1000-0801.2021031.

摘要

现今常用的线性结构视频推荐方法存在推荐结果非个性化、精度低等问题，故开发高精度的个性化视频推荐方法迫在眉睫。提出了一种基于自编码器与多模态数据融合的视频推荐方法，对文本和视觉两种数据模态进行视频推荐。具体来说，所提方法首先使用词袋和TF-IDF方法描述文本数据，然后将所得特征与从视觉数据中提取的深层卷积描述符进行融合，使每个视频文档都获得一个多模态描述符，并利用自编码器构造低维稀疏表示。本文使用 3 个真实数据集对所提模型进行了实验，结果表明，与单模态推荐方法相比，所提方法推荐性能明显提升，且所提视频推荐方法的性能优于基准方法。

Abstract

Nowadays

the commonly used linear structure video recommendation methods have the problems of non-personalized recommendation results and low accuracy

so it is extremely urgent to develop high-precision personalized video recommendation method.A video recommendation method based on the fusion of autoencoders and multi-modal data was presented.This method fused two data including text and vision for video recommendation.To be specific

the method proposed firstly used bag of words and TF-IDF methods to describe text data

and then fused the obtained features with deep convolutional descriptors extracted from visual data

so that each video document could get a multi-modal descriptors

and constructed low-dimensional sparse representation by autoencoders.Experiments were performed on the proposed model by using three real data sets.The result shows that compared with the single-modal recommendation method

the recommendation results of the proposed method are significantly improved

and the performance is better than the reference method.

关键词

Keywords

references

BOBADILLA J , ORTEGA F , HERNANDO A , et al . Recom-mender systems survey [J ] . Knowledge-based systems , 2013 ( 46 ): 109 - 132 .

HU Y , KOREN Y , VOLINSKY C . Collaborative filtering for implicit feedback datasets [C ] // Proceedings of 2008 Eighth IEEE International Conference on Data Mining . Piscataway:IEEE Press , 2008 : 263 - 272 .

LI Z , PENG J Y , GENG G H , et al . Video recommendation based on multi-modal information and multiple kernel [J ] . Mul-timedia Tools and Applications , 2015 , 74 ( 13 ): 4599 - 4616 .

NING X , KARYPIS G . Slim:sparse linear methods for top-n recommender systems [C ] // Proceedings of 2011 IEEE 11th In-ternational Conference on Data Mining . Piscataway:IEEE Press , 2011 : 497 - 506 .

RENDLE S , FREUDENTHALER C , GANTNER Z , et al . Bpr:Bayesian personalized ranking from implicit feedback.UAI’09 [J ] . Arlington,Virginia,United States , 2009 : 452 - 461 .

WANG C , BLEI D M . Collaborative topic modeling for re-commending scientific articles [C ] // Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Dis-covery and Data Mining . New York:ACM Press , 2011 : 448 - 456 .

KABBUR S , NING X , KARYPIS G . Fism:factored item simi-larity models for top-N recommender systems [C ] // Proceedings of the 19th ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining . New York:ACM Press , 2013 : 659 - 667 .

DESHPANDE M , KARYPIS G . Item-based top-n recommenda-tion algorithms [J ] . ACM Transactions on Information Systems , 2004 , 22 ( 1 ): 143 - 177 .

NING X , KARYPIS G . Sparse linear methods with side information for top-N recommendations [C ] // Proceedings of the Sixth ACM Conference on Recommender Systems . New York:ACM Press , 2012 : 155 - 162 .

任永功 , 杨柳 , 刘洋 . 基于热扩散影响力传播的社交网络个性化推荐算法 [J ] . 模式识别与人工智能 , 2019 , 32 ( 8 ): 746 - 757 .

REN Y G , YANG L , LIU Y . Heat diffusion influence propaga-tion based personalized recommendation algorithm for social network [J ] . Pattern Recognition and Artificial Intelligence , 2019 , 32 ( 8 ): 746 - 757 .

RUMELHART D E , HINTON G E , WILLIAMS R J . Learning representations by back-propagating errors [J ] . Nature , 1986 , 323 ( 6088 ): 533 - 536 .

DAVIDSON J , LIEBALD B , LIU J , et al . The YouTube video recommendation system [C ] // Proceedings of the Fourth ACM Conference on Recommender Systems . New York:ACM Press , 2010 : 293 - 296 .

ZHOU R , KHEMMARAT S , GAO L . The impact of YouTube recommendation system on video views [C ] // Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement . New York:ACM Press , 2010 : 404 - 410 .

LINDEN G , SMITH B , YORK J.Amazon . com recommenda-tions:Item-to-item collaborative filtering [J ] . IEEE Internet Computing , 2003 , 7 ( 1 ): 76 - 80 .

AHMED M , IMTIAZ M T , KHAN R . Movie recommendation system using clustering and pattern recognition net-work [C ] // Proceedings of 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) . Piscata-way:IEEE Press , 2018 : 143 - 147 .

BEEL J , GIPP B , LANGER S , et al . paper recommender sys-tems:a literature survey [J ] . International Journal on Digital Li-braries , 2016 , 17 ( 4 ): 305 - 338 .

BOBADILLA J , HERNANDO A , ORTEGA F , et al . Collabora-tive filtering based on significances [J ] . Information Sciences , 2012 , 185 ( 1 ): 1 - 17 .

LAO N , COHEN W W . Relational retrieval using a combination of path-constrained random walks [J ] . Machine Learning , 2010 , 81 ( 1 ): 53 - 67 .

NASCIMENTO C , LAENDER A H F , DA SILVA A S , et al . A source independent framework for research paper recommenda-tion [C ] // Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries . New York:ACM Press , 2011 : 297 - 306 .

KIM H N , JI A T , HA I , et al . Collaborative filtering based on collaborative tagging for enhancing the quality of recommenda-tion [J ] . Electronic Commerce Research and Applications , 2010 , 9 ( 1 ): 73 - 83 .

YANG C , CHEN X , LIU L , et al . A hybrid movie recommenda-tion method based on social similarity and item attributes [C ] // Proceedings of International Conference on Sensing and Imag-ing . Heidelberg:Springer , 2018 : 275 - 285 .

CHRISTAKOU C , VRETTOS S , STAFYLOPATIS A . A hybrid movie recommender system based on neural networks [J ] . In-ternational Journal on Artificial Intelligence Tools , 2007 , 16 ( 5 ): 771 - 792 .

BEUTEL A , COVINGTON P , JAIN S , et al . Latent cross:mak-ing use of context in recurrent recommender sys-tems [C ] // Proceedings of the Eleventh ACM International Con-ference on Web Search and Data Mining . New York:ACM Press , 2018 : 46 - 54 .

黄立威 , 江碧涛 , 吕守业 , 等 . 基于深度学习的推荐系统研究综述 [J ] . 计算机学报 , 2018 , 41 ( 7 ): 1619 - 1647 .

HUANG L W , JIANG B T , LV S Y , et al . A review of recom-mendation Systems based on deep learning [J ] . Journal of Com-puter Science , 2018 , 41 ( 7 ): 1619 - 1647 .

COVINGTON P , ADAMS J , SARGIN E . Deep neural networks for youtube recommendations [C ] // Proceedings of the 10th ACM Conference on Recommender Systems . New York:ACM Press , 2016 : 191 - 198 .

FAN Y , WANG Y , YU H , et al . Movie recommendation based on visual features of trailers [C ] // Proceedings of International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing . Heidelberg:Springer , 2017 : 242 - 253 .

CHENG H T , KOC L , HARMSEN J , et al . Wide ＆ deep learning for recommender systems [C ] // Proceedings of the 1st Workshop on Deep Learning for Recommender Systems .[S.l.:s.n. ] , 2016 : 7 - 10 .

ZHANG Y , AI Q , CHEN X , et al . Joint representation learning for top-N recommendation with heterogeneous information sources [C ] // Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . New York:ACM Press , 2017 : 1449 - 1458 .

LI Z , PENG J Y , GENG G H , et al . Video recommendation based on multi-modal information and multiple kernel [J ] . Multimedia Tools and Applications , 2015 , 74 ( 13 ): 4599 - 4616 .

王娜 , 何晓明 , 刘志强 , 等 . 一种基于用户播放行为序列的个性化视频推荐策略 [J ] . 计算机学报 , 2020 , 43 ( 1 ): 123 - 135 .

WANG N , HE X M , LIU Z Q , et al . Personalized video recom-mendation strategy based on user’s playback behavior se-quence [J ] . Journal of Computer Science , 2020 , 43 ( 1 ): 123 - 135 .

苏赋 , 吕沁 , 罗仁泽 . 基于深度学习的图像分类研究综述 [J ] . 电信科学 , 2019 , 35 ( 11 ): 58 - 74 .

SU F , LV Q , LUO R Z . Review of image classification based on deep learning [J ] . Telecommunications Science , 2019 , 35 ( 11 ): 58 - 74 .

FELIPE L A , CONCEIC A L C , Pádua A L A C , et al . Multi-modal data fusion framework based on autoencoders for top-N recommender systems [J ] . Applied Intelligence , 2019 , 49 ( 9 ) 3267 - 3282 .

CREMONESI P , KOREN Y , TURRIN R . Performance of re-commender algorithms on top-N recommendation tasks [C ] // Proceedings of the fourth ACM conference on Recommender systems . New York:ACM Press , 2010 : 39 - 46 .

LECUN Y , BOSER B , DENKER J S , et al . Backpropagation applied to handwritten zip code recognition [J ] . Neural computa-tion , 1989 , 1 ( 4 ): 541 - 551 .

NASCIMENTO G , LARANIEIRA C , BRAZ V , et al . A robust indoor scene recognition method based on sparse representa-tion [C ] // Proceedings of Iberoamerican Congress on Pattern Recognition . Heidelberg:Springer , 2017 : 408 - 415 .

DEFFERRARD M , BRESSON X , VANDERGHEYNST P . Convolutional neural networks on graphs with fast localized spectral filtering [C ] // Advances in Neural Information Processing Systems .[S.l.:s.n. ] , 2016 : 3844 - 3852 .

YANG J , NGUYEN M N , SAN P P , et al . Deep convolutional neural networks on multichannel time series for human activity recognition [C ] // Proceedings of Twenty-Fourth International Joint Conference on Artificial Intelligence .[S.l.:s.n. ] , 2015 : 3995 - 4001 .

CUNNINGHAM J P , BYRON M Y . Dimensionality reduction for large-scale neural recordings [J ] . Nature Neuroscience , 2014 , 17 ( 11 ): 1500 - 1509 .

BAEZA-YATES R , RIBEIRO-NETO B . Modern information retrieval [M ] . New York : ACM Press , 1999 .

SZEGEDY C , VANHOUCKE V , IOFFE S , et al . Rethinking the inception architecture for computer vision [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 2818 - 2826 .

RUSSAKOVSKY O , DENG J , SU H , et al . Imagenet large scale visual recognition challenge [J ] . International Journal of Computer Vision , 2015 , 115 ( 3 ): 211 - 252 .

LE Q , MIKOLOY T . Distributed representations of sentences and documents [C ] // Proceedings of International Conference on Machine Learning .[S.l.:s.n. ] , 2014 : 1188 - 1196 .

JIA Y , SHELHAMER E , DONAHUE J , et al . Caffe:Convolutional architecture for fast feature embedding [C ] // Proceedings of the 22nd ACM International Conference on Multimedia . New York:ACM Press , 2014 : 675 - 678 .

胡春华 , 童小芹 , 梁伟 . 基于信任和不信任关系的实值受限玻尔兹曼机推荐算法 [J ] . 系统工程理论与实践 , 2019 , 39 ( 7 ): 1817 - 1830 .

HU C H , TONG X X , LIANG W . The real-value restricted Boltzmann machine recommendation algorithm based on trust-distrust relationship [J ] . Systems Engineering-Theory ＆Practice , 2019 , 39 ( 7 ): 1817 - 1830 .

肖云鹏 , 孙华超 , 戴天骥 , 等 . 一种基于云模型的社交网络推荐系统评分预测方法 [J ] . 电子学报 , 2018 , 46 ( 7 ): 1762 - 1767 .

XIAO Y P , SUN H C , DAI T J , et al . A rating prediction method based on cloud model in social recommendation system [J ] . Acta Electronica Sinica , 2018 , 46 ( 7 ): 1762 - 1767 .

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