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Distributed representation learning via node2vec for implicit feedback recommendation

  • Cognitive Computing for Intelligent Application and Service
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Abstract

As an important technology of Internet products, the recommender system can help users to obtain the information they need and alleviate the problem of information overload. In the implicit feedback recommender system, the key issue is how to represent users and products. In recent years, deep learning has achieved good performance in many fields including speech recognition, computer vision and natural language processing. We propose a deep learning-enhanced framework for implicit feedback recommendation. In this framework, we simultaneously learn the new distributed representation of users and items via node2vec to improve the negative sampling strategy. Finally, we develop a deep neural network recommendation model to integrate user features, product features and interaction features. Experiments conducted on two real-world datasets demonstrate the effectiveness of the proposed framework and methods.

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Notes

  1. https://grouplens.org/datasets/hetrec-2011/.

  2. http://www.citeulike.org/faq/data.adp.

  3. https://github.com/aditya-grover/node2vec.

  4. https://keras.io/.

References

  1. 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. ACM, pp 285–295

  2. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 426–434

  3. Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: Eighth IEEE international conference on data mining, 2008. ICDM’08. IEEE, pp 263–272

  4. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR Bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, 2009. AUAI Press, pp 452–461

  5. 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. International world wide web conferences steering committee, pp 173–182

  6. He X, Chua T-S (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, pp 355–364

  7. Ma H, Zhou TC, Lyu MR, King I (2011) Improving recommender systems by incorporating social contextual information. ACM Trans Inf Syst (TOIS) 29(2):9

    Article  Google Scholar 

  8. Yuan W, Li C, Guan D, Han G, Khattak AM (2018) Socialized healthcare service recommendation using deep learning. Neural Comput Appl 7:1–12

    Google Scholar 

  9. Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (2014) GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 831–840

  10. Shi C, Zhang Z, Luo P, Yu PS, Yue Y, Wu B (2015) Semantic path based personalized recommendation on weighted heterogeneous information networks. In: Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, pp 453–462

  11. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  12. Liang D, Charlin L, McInerney J, Blei DM (2016) Modeling user exposure in recommendation. In: Proceedings of the 25th international conference on world wide web. International World Wide Web Conferences Steering Committee, pp 951–961

  13. Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. In: Advances in neural information processing systems, pp 1257–1264

  14. Rendle S, Gantner Z, Freudenthaler C, Schmidt-Thieme L (2011) Fast context-aware recommendations with factorization machines. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. ACM, pp 635–644

  15. Kabbur S, Ning X, Karypis G (2013) Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 659–667

  16. Shi Y, Karatzoglou A, Baltrunas L, Larson M, Hanjalic A, Oliver N (2012) TFMAP: optimizing MAP for top-n context-aware recommendation. In: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 155–164

  17. Zhang Y, Lai G, Zhang M, Zhang Y, Liu Y, Ma S (2014) Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Proceedings of the 37th international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 83–92

  18. Lerche L, Jannach D (2014) Using graded implicit feedback for bayesian personalized ranking. In: Proceedings of the 8th ACM conference on recommender systems. ACM, pp 353–356

  19. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  20. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  21. Kim Y (2014) Convolutional neural networks for sentence classification. arXiv:14085882

  22. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112

  23. Hinton G, Deng L, Yu D, Dahl GE, A-r Mohamed, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97

    Article  Google Scholar 

  24. Amodei D, Ananthanarayanan S, Anubhai R, Bai J, Battenberg E, Case C, Casper J, Catanzaro B, Cheng Q, Chen G (2016) Deep speech 2: end-to-end speech recognition in english and mandarin. In: International conference on machine learning, pp 173–182

  25. 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. ACM, pp 1235–1244

  26. Kim D, Park C, Oh J, Lee S, Yu H (2016) Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp 233–240

  27. Kim D, Park C, Oh J, Yu H (2017) Deep hybrid recommender systems via exploiting document context and statistics of items. Inf Sci 417:72–87

    Article  Google Scholar 

  28. Van den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. In: Advances in neural information processing systems, pp 2643–2651

  29. He R, McAuley J (2016) VBPR: visual bayesian personalized ranking from implicit feedback. In: AAAI, pp 144–150

  30. Yu W, Zhang H, He X, Chen X, Xiong L, Qin Z (2018) Aesthetic-based clothing recommendation. In: Proceedings of the 2018 world wide web conference on world wide web. International World Wide Web Conferences Steering Committee, pp 649–658

  31. Wang Z, Chang S, Dolcos F, Beck D, Liu D, Huang TS (2016) Brain-inspired deep networks for image aesthetics assessment. arXiv:160104155

  32. Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: Encoder-decoder approaches. arXiv:14091259

  33. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. arXiv:1511.06939

  34. Wu C-Y, Ahmed A, Beutel A, Smola AJ, Jing H (2017) Recurrent recommender networks. In: Proceedings of the tenth ACM international conference on web search and data mining. ACM, pp 495–503

  35. Jing H, Smola AJ (2017) Neural survival recommender. In: Proceedings of the tenth ACM international conference on web search and data mining. ACM, pp 515–524

  36. Zhang F, Yuan NJ, Lian D, Xie X, Ma W-Y (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 353–362

  37. Chu W-T, Tsai Y-L (2017) A hybrid recommendation system considering visual information for predicting favorite restaurants. World Wide Web 20(6):1313–1331

    Article  Google Scholar 

  38. Zhang Y, Ai Q, Chen X, Croft WB (2017) Joint representation learning for top-n recommendation with heterogeneous information sources. In: Proceedings of the 2017 ACM on conference on information and knowledge management. ACM, pp 1449–1458

  39. 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:170804617

  40. Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407

    Article  Google Scholar 

  41. Grover A, Leskovec J (2016) node2vec:scalable feature learning for networks. In: ACM SIGKDD International conference on knowledge discovery and data mining, pp 855–864

  42. 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, pp 701–710

  43. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

  44. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv:13013781

  45. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:14126980

  46. Zhuang F, Zhang Z, Qian M, Shi C, Xie X, He Q (2017) Representation learning via Dual-Autoencoder for recommendation. Neural Netw 90:83–89

    Article  Google Scholar 

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

    Google Scholar 

  48. Guo G, Zhang J, Sun Z, Yorke-Smith N (2015) LibRec: a java library for recommender systems. In: UMAP Workshops

  49. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work is supported by the Major Program of the National Natural Science Foundation of China (71490725), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (71521001), the National Natural Science Foundation of China (71872060, 71722010, 91546114, 91746302, 71501057) and The National Key Research and Development Program of China (2017YFB0803303).

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Tunxi Road No. 193, Hefei, Anhui, China

    Yezheng Liu, Zhiqiang Tian, Jianshan Sun, Yuanchun Jiang & Xue Zhang

  2. Key Laboratory of Process Optimization and Intelligent Decision Making, Ministry of Education, Hefei, 230009, Anhui, China

    Yezheng Liu & Zhiqiang Tian

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  1. Yezheng Liu
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  2. Zhiqiang Tian
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  3. Jianshan Sun
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  4. Yuanchun Jiang
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  5. Xue Zhang
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Correspondence to Jianshan Sun.

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Liu, Y., Tian, Z., Sun, J. et al. Distributed representation learning via node2vec for implicit feedback recommendation. Neural Comput & Applic 32, 4335–4345 (2020). https://doi.org/10.1007/s00521-018-03964-2

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