Survey on Deep Learning Based News Recommendation Algorithm

doi:10.3778/j.issn.1673-9418.2007021

Abstract

Abstract:

News recommendation (NR) can effectively alleviate the overload of news information, and it is an important way to obtain news information for users. Deep learning (DL) has become a mainstream technology to promote the development of NR in recent years, and the effect of news recommendation has been significantly improved, which is widely concerned by researchers. In this paper, the methods of deep learning-based news recommendation (DNR) are classified, analyzed and summarized. In the research of NR, modeling users or news are two key tasks. According to different strategies of modeling users or news, the news recommendation methods based on deep learning are divided into three types: “two-stage” method, “fusion” method and “collaboration” method. Each type of method is further subdivided in terms of sub-tasks or the data organization structure based on. The representative models of each method are introduced and analyzed, and their advantages and limitations are evaluated. The characteristics, advantages and disadvantages of each type of methods are also summarized in detail. Furthermore, the commonly used datasets, baseline and performance evaluation indicators are introduced. Finally, the possible future research directions and development trends in this field are analyzed and predicted.

Key words: news recommendation (NR), deep learning (DL), user interest modeling, news modeling

摘要：

新闻推荐（NR）可以有效缓解新闻信息过载，是当今人们获取新闻资讯的重要方式，而深度学习（DL）成为近年来促进新闻推荐发展的主流技术，使新闻推荐的效果得到显著提升，受到研究者们的广泛关注。主要对基于深度学习的新闻推荐方法研究现状进行分类梳理和分析归纳。根据对新闻推荐的核心对象——用户和新闻的建模思路不同，将基于深度学习的新闻推荐方法分为“两段式”方法、“融合式”方法和“协同式”方法三类。在每类方法中，根据建模过程中的具体子任务或基于的数据组织结构进行更进一步细分，对代表性模型进行分析介绍，评价其优点和局限性等，并详细总结每类方法的特点和优缺点。另外还介绍了新闻推荐中常用数据集、基线算法和性能评价指标，最后分析展望了该领域未来可能的研究方向及发展趋势。

TIAN Xuan, DING Qi, LIAO Zihui, SUN Guodong. Survey on Deep Learning Based News Recommendation Algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 971-998.

田萱, 丁琪, 廖子慧, 孙国栋. 基于深度学习的新闻推荐算法研究综述[J]. 计算机科学与探索, 2021, 15(6): 971-998.

References

[1] PRAWESH S, PADMANABHAN B. The “top N” news recommender: count distortion and manipulation resistance[C]//Proceedings of the 2011 ACM Conference on Recommender Systems, Chicago, Oct 23-27, 2011. New York: ACM, 2011: 237-244.
[2] GULLA J A, YU B, ?ZG?BEK ?, et al. 3rd International Workshop on News Recommendation and Analytics (INRA 2015)[C]//Proceedings of the 9th ACM Conference on Recommender Systems, Vienna, Sep 16-20, 2015. New York: ACM, 2015: 345-346.
[3] ZHAO X, WANG C, CHEN M, et al. AutoEmb: automated embedding dimensionality search in streaming recommendations[J]. arXiv:2002.11252, 2020.
[4] XU J, HE X N, LI H. Deep learning for matching in search and recommendation[C]//Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, Jul 8-12, 2018. New York: ACM, 2018: 1365-1368.
[5] KAZAI G, YUSOF I, CLARKE D. Personalised news and blog recommendations based on user location, facebook and Twitter user profiling[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Jul 17-21, 2016. New York: ACM, 2016: 1129-1132.
[6] KARKALI M, PONTIKIS D, VAZIRGIANNIS M. Match the news: a firefox extension for real-time news recommendation[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Jul 28 - Aug 1, 2013: 1117-1118.
[7] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[8] ZHANG S, YAO L, SUN A, et al. Deep learning based recommender system: a survey and new perspectives[J]. ACM Computing Surveys, 2019, 52(1): 1-38.
[9] HUANG L W, JIANG B T, LV S Y, et al. Survey on deep learning based recommender systems[J]. Chinese Journal of Computers, 2018, 41(7): 1619-1647.
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.
[10] WANG S Q, LI X X, SUN F Z, et al. Survey of research on personalized news recommendation techniques[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(1): 18-29.
王绍卿, 李鑫鑫, 孙福振, 等. 个性化新闻推荐技术研究综述[J]. 计算机科学与探索, 2020, 14(1): 18-29.
[11] LI M, WANG L. A survey on personalized news recommendation technology[J]. IEEE Access, 2019, 7: 145861-145879.
[12] LIANG S W, ZHANG C R, CAO L, et al. Collaborative joint embedding based on personalized news recommendation[J]. Journal of Chinese Information Processing, 2018, 32(11): 72-78.
梁仕威, 张晨蕊, 曹雷, 等. 基于协同表示学习的个性化新闻推荐[J]. 中文信息学报, 2018, 32(11): 72-78.
[13] LIU J H, DOLAN P, PEDERSEN E R. Personalized news recommendation based on click behavior[C]//Proceedings of the 15th International Conference on Intelligent User Interfaces, Hong Kong, China, Feb 7-10, 2010. New York: ACM, 2010: 31-40.
[14] KELLY D, TEEVAN J. Implicit feedback for inferring user preference: a bibliography[J]. SIGIR Forum, 2003, 37(2): 18-28.
[15] WU X, HUANG B, FANG Z J, et al. Application of sequence generative adversarial network in recommendation system[J]. Computer Engineering and Applications, 2020, 56(23): 175-179.
伍鑫, 黄渤, 方志军, 等. 序列生成对抗网络在推荐系统中的应用[J]. 计算机工程与应用, 2020, 56(23): 175-179.
[16] PENG Y, ZHU W, ZHAO Y, et al. Cross-media analysis and reasoning: advances and directions[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 44-57.
[17] CHAUDHARI S, POLATKAN G, RAMANATH R, et al. An attentive survey of attention models[J]. arXiv:1904. 02874, 2019.
[18] WANG X J, YU L T, REN K, et al. Dynamic attention deep model for article recommendation by learning human editors?? demonstration[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Aug 13-17, 2017. New York: ACM, 2017: 2051-2059.
[19] ZHANG L, LIU P, GULLA J A. A deep joint network for session-based news recommendations with contextual augmentation[C]//Proceedings of the 29th on Hypertext and Social Media, Baltimore, Jul 9-12, 2018. New York: ACM, 2018: 201-209.
[20] DE SOUZA PEREIRA MOREIRA G. CHAMELEON: a deep learning meta-architecture for news recommender systems[C]//Proceedings of the 12th ACM Conference on Recommender Systems, Vancouver, Oct 2-7, 2018. New York: ACM, 2018: 578-583.
[21] ZHANG L, LIU P, GULLA J A. Dynamic attention-integrated neural network for session-based news recommendation[J]. Machine Learning, 2019, 108(10): 1851-1875.
[22] ZHU Q N, ZHOU X F, SONG Z L, et al. DAN: deep attention neural network for news recommendation[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 5973-5980.
[23] OKURA S, TAGAMI Y, ONO S, et al. Embedding-based news recommendation for millions of users[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Aug 13- 17, 2017. New York: ACM, 2017: 1933-1942.
[24] WU C H, WU F Z, AN M X, et al. NPA: neural news recommendation with personalized attention[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 2576-2584.
[25] AN M X, WU F Z, WU C H, et al. Neural news recommendation with long-and short-term user representations[C]//Proceedings of the 57th Conference of the Association for Computational Linguistics, Florence, Jul 28-Aug 2, 2019. Stroudsburg: ACL, 2019: 336-345.
[26] WU C, WU F, AN M, et al. Neural news recommendation with attentive multi-view learning[J]. arXiv:1907.05576, 2019.
[27] WU C, WU F, GE S, et al. Neural news recommendation with multi-head self-attention[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, Nov 3-7, 2019. Stroudsburg: ACL, 2019: 6388-6393.
[28] WU C H, WU F Z, QI T, et al. User modeling with click preference and reading satisfaction for news recommendation [C]//Proceedings of the 29th International Joint Conference on Artificial Intelligence, Yokohama, Jul 2020: 3023-3029.
[29] YANG B, MITCHELL T. Leveraging knowledge bases in LSTMs for improving machine reading[J]. arXiv:1902. 09091, 2019.
[30] WANG J, WANG Z Y, ZHANG D W, et al. Combining knowledge with deep convolutional neural networks for short text classification[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Aug 19-25, 2017: 2915-2921.
[31] XU C, BAI Y L, BIAN J, et al. RC-NET: a general framework for incorporating knowledge into word representations[C]//Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, Shanghai, Nov 3-7, 2014. New York: ACM, 2014: 1219-1228.
[32] WANG H W, ZHANG F Z, XIE X, et al. DKN: deep know-ledge-aware network for news recommendation[C]//Proceedings of the 2018 World Wide Web Conference, Lyon, Apr 23-27, 2018. New York: ACM, 2018: 1835-1844.
[33] GAO J, XIN X, LIU J S, et al. Fine-grained deep knowledge- aware network for news recommendation with self-attention[C]//Proceedings of the 2018 IEEE/WIC/ACM International Conference on Web Intelligence, Santiago, Dec 3-6, 2018. Washington: IEEE Computer Society, 2018: 81-88.
[34] XU B B, CEN K T, HUANG J J, et al. A survey on graph convolutional neural network[J]. Chinese Journal of Computers, 2020, 43(5): 755-780.
徐冰冰, 岑科廷, 黄俊杰, 等. 图卷积神经网络综述[J]. 计算机学报, 2020, 43(5): 755-780.
[35] SHEU H S, LI S. Context-aware graph embedding for session-based news recommendation[C]//Proceedings of the 14th ACM Conference on Recommender Systems, Virtual Event, Sep 22-26, 2020: 657-662.
[36] LEE D, OH B, SEO S, et al. News recommendation with topic-enriched knowledge graphs[C]//Proceedings of the 29th ACM International Conference on Information and Know-ledge Management, Virtual Event, Oct 19-23, 2020. New York: ACM, 2020: 695-704.
[37] COVINGTON P, ADAMS J, SARGIN E. Deep neural networks for YouTube recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems, Boston, Sep 15-19, 2016. New York: ACM, 2016: 191-198.
[38] WANG X, HE X N, NIE L Q, et al. Item silk road: recommending items from information domains to social users[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Aug 7-11, 2017. New York: ACM, 2017: 185-194.
[39] LIAN J X, ZHANG F Z, XIE X, et al. Towards better representation learning for personalized news recommendation: a multi-channel deep fusion approach[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence, Jul 13-19, 2018: 3805-3811.
[40] WANG H Y, WU F Z, LIU Z, et al. Fine-grained interest matching for neural news recommendation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 5-10, 2020. Stroudsburg: ACL, 2020: 836-845.
[41] GOODFELLOW I, BENGIO Y, COURVILLE A. Deep learning[M]. Cambridge: MIT Press, 2016.
[42] PARK K, LEE J, CHOI J. Deep neural networks for news recommendations[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, Nov 6-10, 2017. New York: ACM, 2017: 2255-2258.
[43] KHATTAR D, KUMAR V, VARMA V, et al. HRAM: a hybrid recurrent attention machine for news recommendation[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 1619-1622.
[44] CHU Q F, LIU G S, SUN H R, et al. Next news recommendation via knowledge-aware sequential model[C]//LNCS 11856: Proceedings of the 18th China National Conference on Chinese Computational Linguistics, Kunming, Oct 18-20, 2019. Berlin, Heidelberg: Springer, 2019: 221-232.
[45] KUMAR V, KHATTAR D, GUPTA S, et al. Word semantics based 3-d convolutional neural networks for news recommendation[C]//Proceedings of the 2017 IEEE International Conference on Data Mining Workshops, New Orleans, Nov 18-21, 2017. Washington: IEEE Computer Society, 2017: 761-764.
[46] JI S, XU W, YANG M, et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35(1): 221-231.
[47] KHATTAR D, KUMAR V, VARMA V, et al. Weave&Rec: a word embedding based 3-d convolutional network for news recommendation[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 1855-1858.
[48] CAO S X, YANG N, LIU Z Z. Online news recommender based on stacked auto-encoder[C]//Proceedings of the 16th IEEE/ACIS International Conference on Computer and Information Science, Wuhan, May 24-26, 2017. Washington: IEEE Computer Society, 2017: 721-726.
[49] HU L M, XU S Y, LI C, et al. Graph neural news recommendation with unsupervised preference disentanglement[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 5-10, 2020. Stroudsburg: ACL, 2020: 4255-4264.
[50] BENGIO Y, COURVILLE A, VINCENT P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828.
[51] GE S Y, WU C H, WU F Z, et al. Graph enhanced representation learning for news recommendation[C]//Proceedings of the Web Conference, Taipei, China, Apr 20-24, 2020. New York: ACM, 2020: 2863-2869.
[52] GULLA J A, ZHANG L M, LIU P, et al. The Adressa dataset for news recommendation[C]//Proceedings of the 2017 International Conference on Web Intelligence, Leipzig, Aug 23-26, 2017. New York: ACM, 2017: 1042-1048.
[53] KILLE B, HOPFGARTNER F, BRODT T, et al. The plista dataset[C]//Proceedings of the 2013 International News Recommender Systems Workshop and Challenge, Oct 2013. New York: ACM, 2013: 16-23.
[54] CANTADOR I, BRUSILOVSKY P, KUFLIK T. Second workshop on information heterogeneity and fusion in recommender systems[C]//Proceedings of the 2011 ACM Conference on Recommender Systems, Chicago, Oct 23-27, 2011. New York: ACM, 2011: 387-388.
[55] HARPER F M, KONSTAN J A. The MovieLens datasets: history and context[J]. ACM Transactions on Interactive Intelligent Systems, 2015, 5(4): 19.
[56] WU F Z, QIAO Y, CHEN J H, et al. Mind: a large-scale dataset for news recommendation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 5-10, 2020. Stroudsburg: ACL, 2020: 3597-3606.
[57] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International World Wide Web Conference, Hong Kong, China, May 1-5, 2001. New York: ACM, 2001: 285-295.
[58] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[J]. arXiv:1205.2618, 2012.
[59] RENDLE S. Factorization machines with libFM[J]. ACM Transactions on Intelligent Systems and Technology, 2012, 3(3): 1-22.
[60] HUANG P S, HE X D, GAO J F, et al. Learning deep structured semantic models for web search using clickthrough data[C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, San Francisco, Oct 27-Nov 1, 2013. New York: ACM, 2013: 2333-2338.
[61] ZHENG G J, ZHANG F Z, ZHENG Z H, et al. DRN: a deep reinforcement learning framework for news recommendation[C]//Proceedings of the 2018 World Wide Web Conference, Lyon, Apr 23-27, 2018. New York: ACM, 2018: 167-176.
[62] CHENG H, KOC L, HARMSEN J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, Sep 15, 2016. New York: ACM, 2016: 7-10.
[63] GUO H, TANG R, YE Y, et al. DeepFM: a factorization-machine based neural network for CTR prediction[J]. arXiv:1703.04247, 2017.
[64] KUMAR V, KHATTAR D, GUPTA S, et al. Deep neural architecture for news recommendation[C]//Proceedings of the Conference and Labs of the Evaluation Forum, Dublin, Sep 11-14, 2017: 1-19.
[65] QUADRANA M, KARATZOGLOU A, HIDASI B, et al. Personalizing session-based recommendations with hierarchical recurrent neural networks[C]//Proceedings of the 11th ACM Conference on Recommender Systems, Como, Aug 27-31, 2017. New York: ACM, 2017: 130-137.
[66] 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, Perth, Apr 3-7, 2017. New York: ACM, 2017: 173-182.
[67] ZHU Y X, Lv L Y. Evaluation metrics for recommender systems[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(2): 163-175.
朱郁筱, 吕琳媛. 推荐系统评价指标综述[J]. 电子科技大学学报, 2012, 41(2): 163-175.
[68] HANLEY J A, MCNEIL B J. The meaning and use of the area under a receiver operating characteristic (ROC) curve[J]. Radiology, 1982, 143(1): 29-36.
[69] MENG X W, CHEN C, ZHANG Y J. A survey of mobile news recommend techniques and applications[J]. Chinese Journal of Computers, 2016, 39(4): 685-703.
孟祥武, 陈诚, 张玉洁. 移动新闻推荐技术及其应用研究综述[J]. 计算机学报, 2016, 39(4): 685-703.
[70] QIN X P. Causes and countermeasures of false news: taking the top ten fake news in 2017 as an example[J]. New Media Research, 2018, 4(13): 19-20.
秦希屏. 虚假新闻的成因及其应对策略——以2017年十大假新闻为例[J]. 新媒体研究, 2018, 4(13): 19-20.
[71] RUCHANSKY N, SEO S, LIU Y. CSI: a hybrid deep model for fake news detection[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, Nov 6-10, 2017. New York: ACM, 2017: 797-806.
[72] ZHANG J, CUI L, FU Y, et al. Fake news detection with deep diffusive network model[J]. arXiv:1805.08751, 2018.
[73] LU H Y, ZHANG M, MA W Z, et al. Quality effects on user preferences and behaviors in mobile news streaming [C]//Proceedings of the 28th International World Wide Web Conference, San Francisco, May 13-17, 2019. New York: ACM, 2019: 1187-1197.
[74] ZHANG H G, HAN W B, LAI X J, et al. A survey on cyberspace security[J]. Scientia Sinica (Informationis), 2016, 46(2): 125-164.
张焕国, 韩文报, 来学嘉, 等. 网络空间安全综述[J]. 中国科学: 信息科学, 2016, 46(2): 125-164.
[75] ZHANG Y, CHEN X. Explainable recommendation: a survey and new perspectives[J]. arXiv:1804.11192, 2018.
[76] LU Y C, DONG R H, SMYTH B. Coevolutionary recommendation model: mutual learning between ratings and reviews[C]//Proceedings of the 2018 World Wide Web Conference, Lyon, Apr 23-27, 2018. New York: ACM, 2018: 773-782.
[77] WANG H W, ZHANG F Z, WANG J, et al. RippleNet: propagating user preferences on the knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 417-426.