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The graph-based behavior-aware recommendation for interactive news

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Abstract

Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user’s behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging user’s preferences. Further, although heterogeneous graphs have been applied in different areas, a proper way to construct a heterogeneous graph for interactive news data with an appropriate learning mechanism on it is still desired. To address the above concerns, we propose a graph-based behavior-aware network, which simultaneously considers six different types of behaviors as well as user’s demand on the news diversity. We have three mainsteps. First, we build an interaction behavior graph for multi-level and multi-category data. Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM to learn behavior sequence representations. Third, we introduce core and coritivity features for the behavior graph, which measure the concentration degree of user’s interests. These features affect the trade-off between accuracy and diversity of our personalized recommendation system. Taking these features into account, our system finally achieves recommending news to different users at their different levels of concentration degrees.

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Notes

  1. It is not needed for English news.

  2. http://pypi.python.org/pypi/jieba/.

  3. https://github.com/Embedding/Chinese-Word-Vectors.

  4. https://grouplens.org/datasets/movielens/.

References

  1. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence, UAI’98, page 43-52, San Francisco, CA, USA. Morgan Kaufmann publishers Inc

  2. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, pages 285–295

  3. Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72

    Article  Google Scholar 

  4. Basu C, Hirsh H, Cohen W, et al. (1998) Recommendation as classification: using social and content-based information in recommendation. In: Aaai/iaai, pages 714–720

  5. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web, pages 325–341. Springer

  6. Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Inter 12(4):331–370

    Article  Google Scholar 

  7. Kardan AA, Ebrahimi M (2013) A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups. Inf Sci 219:93–110

    Article  Google Scholar 

  8. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  9. Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using markov chain Monte Carlo. In: Proceedings of the 25th international conference on machine learning, pages 880–887

  10. Shin D, Cetintas S, Lee K-C, Dhillon IS (2015) Tumblr blog recommendation with boosted inductive matrix completion. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pages 203–212

  11. Na S, Luo Y, Yang Z, Wang Z, Kolar M (2020) Semiparametric nonlinear bipartite graph representation learning with provable guarantees. 37th international conference on machine learning

  12. Bell RM, Koren Y (2007) Improved neighborhood-based collaborative filtering. In: KDD cup and workshop at the 13th ACM SIGKDD international conference on knowledge discovery and data mining, pages 7–14. Citeseer

  13. Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: recommender systems handbook, pages 1–34. Springer

  14. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. 3rd international conference on learning representations

  15. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778

  16. Zhang Y, Chan W, Jaitly N (2017) Very deep convolutional networks for end-to-end speech recognition. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4845–4849. IEEE

  17. Papineni K, Roukos S, Ward T, Zhu W-J (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318

  18. Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of the 2014 Conference on empirical methods in natural language processing (EMNLP). Association for Computational Linguistics

  19. Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. 3rd international conference on learning representations

  20. Nakayama H, Nishida N (2017) Zero-resource machine translation by multimodal encoder–decoder network with multimedia pivot. Mach Transl 31(1–2):49–64

    Article  Google Scholar 

  21. Okura S, Tagami Y, Ono S, Tajima A (2017) Embedding-based news recommendation for millions of users. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1933–1942

  22. Wang H, Wang N, Yeung D-Y (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 1235–1244

  23. Guo H, Tang R, Ye Y, Li Z, He X (2017) Deepfm: a factorization-machine based neural network for ctr prediction. 26th international joint conference on artificial intelligence (IJCAI-17)

  24. Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems, pages 191–198

  25. He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pages 173–182

  26. He X, Chua TS (2017) Neural factorization machines for sparse predictive analytics. In: International ACM Sigir Conference

  27. Xiao J, Ye H, He X, Zhang H, Wu F, Chua T-S (2017) Attentional factorization machines: learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617

  28. Zhu Q, Zhou X, Song Z, Tan J, Guo L (2019) Dan: Deep attention neural network for news recommendation. Proc AAAI Conf Artif Intell 33:5973–5980

    Google Scholar 

  29. Wang X, He X, Cao Y, Liu M, Chua T-S (2019) Kgat: Knowledge graph attention network for recommendation. In: proceedings of the 25th ACM SIGKDD international conference on Knowledge Discovery & Data Mining, pages 950–958

  30. Javari A, Jalili M (2015) A probabilistic model to resolve diversity–accuracy challenge of recommendation systems. Knowl Inf Syst 44(3):609–627

    Article  Google Scholar 

  31. Zhang J, Yu PS (2018) Broad learning: an emerging area in social network analysis. ACM SIGKDD Explor Newsl 20(1):24–50

    Article  Google Scholar 

  32. Wu Y, Yang Y, Jiang F, Jin S, Xu J (2014) Coritivity-based influence maximization in social networks. Physica A Stat Mech Appl 416:467–480

    Article  Google Scholar 

  33. Xiong C, Power R, Callan J (2017) Explicit semantic ranking for academic search via knowledge graph embedding. In: Proceedings of the 26th international conference on world wide web, pages 1271–1279

  34. Jin X (1999) The core and coritivity of a system (vii)–subcore and an algorithm of coritivity. J Syst Eng 3

  35. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pages 701–710

  36. Bobadilla J, Ortega F, Gutiérrez A, Alonso S (2020) Classification-based deep neural network architecture for collaborative filtering recommender systems. Int J Interact Multimedia Artif Intell 6(1):68

    Google Scholar 

  37. R Hurtado, J Bobadilla, A Gutiérrez, S Alonso. A collaborative filtering probabilistic approach for recommendation to large homogeneous and automatically detected groups. Int J Interact Multimedia Artif Intell 6(2):1, 2020

  38. Lian J, Zhang F, Xie X, Sun G (2018) Towards better representation learning for personalized news recommendation: a multi-channel deep fusion approach. In: IJCAI, pages 3805–3811

  39. Wang H, Zhang F, Xie X, Guo M (2018) Dkn: deEp knowledge-aware network for news recommendation. In: proceedings of the 2018 world wide web conference, pages 1835–1844

  40. Alonso-Virgos L, Espada JP, González R (2019) Crespo. Analyzing compliance and application of usability guidelines and recommendations by web developers. Comput Stand Interfaces 64:117–132

    Article  Google Scholar 

  41. Alonso-Virgós L, Espada JP, Thomaschewski J, Crespo RG (2020) Test usability guidelines and follow conventions. Useful recommendations from web developers. Comput Stand Interfaces 70:103423

    Article  Google Scholar 

  42. Wan M, McAuley J (2018) Item recommendation on monotonic behavior chains. In: Proceedings of the 12th ACM conference on recommender systems, pages 86–94

  43. Gao C, He X, Gan D, Chen X, Feng F, Li Y, Chua T-S, Jin D (2019) Neural multi-task recommendation from multi-behavior data. In: 2019 IEEE 35th international conference on data engineering (ICDE), pages 1554–1557. IEEE

  44. Zhang K, Jiang F, Zuo Y, Niu Y (2016) Structural vulnerability analysis in complex networks based on core theory. In: 2016 IEEE first international conference on data science in cyberspace (DSC), pages 149–154. IEEE

  45. Zhang Y, Zhang K, Zou X (2018) Structural brain network mining based on core theory. In: 2018 IEEE third international conference on data science in cyberspace (DSC), pages 161–168. IEEE

  46. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. 1st international conference on learning representations (workshop poster)

  47. Morin F, Bengio Y (2005) Hierarchical probabilistic neural network language model. In: International conference on artificial intelligence and statistics, volume 5, pages 246–252. Citeseer

  48. Mnih A, Hinton GE (2009) A scalable hierarchical distributed language model. In: Advances in neural information processing systems, pages 1081–1088

  49. Harper FM, Konstan JA (2015) The movielens datasets: history and context. ACM Trans Interact Intell Syst (tiis) 5(4):1–19

    Google Scholar 

  50. Su Y, Li X, Zha D, Tang W, Gao N (2019) HRec: Heterogeneous Graph Embedding-Based Personalized Point-of-Interest Recommendation

  51. Dong Y, Tang J, Wu S, Tian J, Chawla NV, Rao J, Cao H (2013) Link prediction and recommendation across heterogeneous social networks. In: IEEE International Conference on Data Mining

  52. Ma M, Na S, Wang H (2021) AEGCN: an autoencoder-constrained graph convolutional network. Neurocomputing 432:21–31

    Article  Google Scholar 

  53. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. 5th International conference on learning representations

  54. Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 855–864. ACM

  55. Stützle T, Hoos H (1998) Improvements on the ant-system: introducing the max-min ant system. In: Artificial neural nets and genetic algorithms, pages 245–249. Springer

  56. Stützle T, Hoos HH (2000) Max–min ant system. Futur Gener Comput Syst 16(8):889–914

    Article  Google Scholar 

  57. Li J, Fan Q, Zhang K (2007) Keyword extraction based on tf/idf for Chinese news document. Wuhan Univ J Nat Sci 12:917–921

    Article  Google Scholar 

  58. Rendle S (2010) Factorization machines. In: Data Mining (ICDM), 2010 IEEE 10th international conference on, pages 995–1000. IEEE

  59. Rendle S (2012) Factorization machines with libfm. ACM Trans Intell Syst Technol (TIST) 3(3):57

    Google Scholar 

  60. Rajaraman A, Ullman JD (2011) Mining of massive datasets. Cambridge University Press

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Acknowledgements

The work is supported by the National Key R&D Program of China (2019YFA0706401), National Natural Science Foundation of China (61802009). Mingyuan Ma is partially supported by Beijing development institute at Peking University through award PkuPhD2019006. Sen Na is supported by Harper Dissertation Fellowship from UChicago.

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Authors and Affiliations

  1. School of Electronics Engineering and Computer Science, Peking University, Beijing, China

    Mingyuan Ma, Hongyu Wang, Congzhou Chen & Jin Xu

  2. Department of Statistics, University of Chicago, Chicago, IL, USA

    Sen Na

  3. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China

    Hongyu Wang

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  1. Mingyuan Ma
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  2. Sen Na
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  3. Hongyu Wang
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  4. Congzhou Chen
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  5. Jin Xu
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Correspondence to Hongyu Wang or Jin Xu.

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Ma, M., Na, S., Wang, H. et al. The graph-based behavior-aware recommendation for interactive news. Appl Intell 52, 1913–1929 (2022). https://doi.org/10.1007/s10489-021-02497-x

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