Social movie recommender system based on deep autoencoder network using Twitter data

Abstract

Recommender systems attempt to provide effective suggestions to each user based on their interests and behaviors. These recommendations usually match the personal user preferences and assist them in the decision-making process. With the ever-expanding growth of information on the web, online education systems, e-commerce, and, eventually, the emergence of social networks, the necessity of developing such systems is unavoidable. Collaborative filtering and content-based filtering are among the most important techniques used in recommender systems. Meanwhile, with the significant advances in deep learning in recent years, the use of this technology has been widely observed in recommender systems. In this study, a hybrid social recommender system utilizing a deep autoencoder network is introduced. The proposed approach employs collaborative and content-based filtering, as well as users’ social influence. The social influence of each user is calculated based on his/her social characteristics and behaviors on Twitter. For the evaluation purpose, the required datasets have been collected from MovieTweetings and Open Movie Database. The evaluation results show that the accuracy and effectiveness of the proposed approach have been improved compared to the other state-of-the-art methods.

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References

  1. 1.

    Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132

    Article  Google Scholar 

  2. 2.

    Kunaver M, Požrl T (2017) Diversity in recommender systems—a survey. Knowl Based Syst 123:154–162

    Article  Google Scholar 

  3. 3.

    Yang X, Guo Y, Liu Y, Steck H (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41:1–10

    Article  Google Scholar 

  4. 4.

    Mochón M-C (2016) Social network analysis and big data tools applied to the systemic risk supervision. Int J Interactive Multimed Artif Intell 3(6):34–37

    Google Scholar 

  5. 5.

    Lies J (2019) Marketing intelligence and Big Data: digital Marketing techniques on their way to becoming social engineering techniques in marketing. Int J Interactive Multimed Artif Intell 5(5):134–144

    Google Scholar 

  6. 6.

    Crespo RG, Martínez OS, Lovelle JMC, García-Bustelo BCP, Gayo JEL, De Pablos PO (2011) Recommendation system based on user interaction data applied to intelligent electronic books. Comput Hum Behav 27(4):1445–1449

    Article  Google Scholar 

  7. 7.

    Li J, Xu W, Wan W, Sun J (2018) Movie recommendation based on bridging movie feature and user interest. J Comput Sci 26:128–134

    Article  Google Scholar 

  8. 8.

    Guzmán de Núñez X, Núñez Valdéz ER, Pascual Espada J, González Crespo R, García Díaz V (2018) A proposal for sentiment analysis on twitter for tourism-based applications. In: Fujita H, Herrera-Viedma E (eds) New trends in intelligent software methodologies, tools and techniques. IOS Press, Amsterdam, pp 713–722

    Google Scholar 

  9. 9.

    Guy I (2015) Social recommender systems. In: Kantor PB, Ricci F, Shapira B, Rokach L (eds) Recommender systems handbook. Springer, Berlin, pp 511–543

    Google Scholar 

  10. 10.

    Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1–38

    Google Scholar 

  11. 11.

    Ouyang Y, Liu W, Rong W, Xiong Z (2014) Autoencoder-based collaborative filtering. In: International conference on neural information processing. Springer, Berlin, pp 284–291

  12. 12.

    Chen Y, de Rijke M (2018) A collective variational autoencoder for top-n recommendation with side information. In: Proceedings of the 3rd workshop on deep learning for recommender systems. ACM, pp 3–9

  13. 13.

    Li X, She J (2017) Collaborative variational autoencoder for recommender systems. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 305–314

  14. 14.

    Liang D, Krishnan RG, Hoffman MD, Jebara T (2018) Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 world wide web conference, 2018. International World Wide Web Conferences Steering Committee, pp 689–698

  15. 15.

    Jhamb Y, Ebesu T, Fang Y (2018) Attentive contextual denoising autoencoder for recommendation. In: Proceedings of the 2018 ACM SIGIR international conference on theory of information retrieval. ACM, pp 27–34

  16. 16.

    Wang M, Wu Z, Sun X, Feng G, Zhang B (2019) Trust-aware collaborative filtering with a denoising autoencoder. Neural Process Lett 49(2):835–849

    Article  Google Scholar 

  17. 17.

    Wang K, Xu L, Huang L, Wang C-D, Lai J-H (2019) SDDRS: stacked discriminative denoising auto-encoder based recommender system. Cogn Syst Res 55:164–174

    Article  Google Scholar 

  18. 18.

    Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the ninth ACM international conference on web search and data mining. ACM, pp 153–162

  19. 19.

    Li S, Fu Y (2017) Robust representations for collaborative filtering. Robust representation for data analytics. Springer, Berlin, pp 123–146

    Google Scholar 

  20. 20.

    Li S, Kawale J, Fu Y (2015) Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, pp 811–820

  21. 21.

    Rifai S, Vincent P, Muller X, Glorot X, Bengio Y (2011) Contractive auto-encoders: explicit invariance during feature extraction. In: Proceedings of the 28th international conference on international conference on machine learning. Omnipress, pp 833–840

  22. 22.

    Zhang S, Yao L, Xu X (2017) Autosvd++: an efficient hybrid collaborative filtering model via contractive auto-encoders. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 957–960

  23. 23.

    Capdevila J, Arias M, Arratia A (2016) GeoSRS: a hybrid social recommender system for geolocated data. Inf Syst 57:111–128

    Article  Google Scholar 

  24. 24.

    López-Quintero JF, Lovelle JC, Crespo RG, García-Díaz V (2018) A personal knowledge management metamodel based on semantic analysis and social information. Soft Comput 22(6):1845–1854

    Article  Google Scholar 

  25. 25.

    Das N, Borra S, Dey N, Borah S (2018) Social networking in web based movie recommendation system. In: Dey N, Babo R, Ashour AS, Bhatnagar V, Bouhlel MS (eds) Social networks science: design, implementation, security, and challenges. Springer, Berlin, pp 25–45

    Google Scholar 

  26. 26.

    Li F, Xu G, Cao L (2016) Two-level matrix factorization for recommender systems. Neural Comput Appl 27(8):2267–2278

    Article  Google Scholar 

  27. 27.

    Behera DK, Das M, Swetanisha S (2019) Predicting users’ preferences for movie recommender system using restricted Boltzmann machine. In: Della Riccia G, Kruse R, Lenz H-J (eds) Computational intelligence in data mining. Springer, Berlin, pp 759–769

    Google Scholar 

  28. 28.

    Deldjoo Y, Elahi M, Quadrana M, Cremonesi P (2018) Using visual features based on MPEG-7 and deep learning for movie recommendation. Int J Multimed Inf Retrieval 7(4):207–219

    Article  Google Scholar 

  29. 29.

    Wei J, He J, Chen K, Zhou Y, Tang Z (2017) Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst Appl 69:29–39

    Article  Google Scholar 

  30. 30.

    Pattanayak S (2017) Unsupervised learning with restricted Boltzmann machines and auto-encoders. Pro deep learning with tensorflow. Springer, Berlin, pp 279–343

    Google Scholar 

  31. 31.

    Sun Y, Mao H, Sang Y, Yi Z (2017) Explicit guiding auto-encoders for learning meaningful representation. Neural Comput Appl 28(3):429–436

    Article  Google Scholar 

  32. 32.

    Sun Y, Mao H, Guo Q, Yi Z (2016) Learning a good representation with unsymmetrical auto-encoder. Neural Comput Appl 27(5):1361–1367

    Article  Google Scholar 

  33. 33.

    Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  34. 34.

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

  35. 35.

    Cheng H-T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M (2016) Wide & deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems. ACM, pp 7–10

  36. 36.

    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, ACM, pp 1933–1942

  37. 37.

    Li H, Cui J, Shen B, Ma J (2016) An intelligent movie recommendation system through group-level sentiment analysis in microblogs. Neurocomputing 210:164–173

    Article  Google Scholar 

  38. 38.

    Sun Z, Han L, Huang W, Wang X, Zeng X, Wang M, Yan H (2015) Recommender systems based on social networks. J Syst Softw 99:109–119

    Article  Google Scholar 

  39. 39.

    Seo Y-D, Kim Y-G, Lee E, Baik D-K (2017) Personalized recommender system based on friendship strength in social network services. Expert Syst Appl 69:135–148

    Article  Google Scholar 

  40. 40.

    Zhao Z, Yang Q, Lu H, Weninger T, Cai D, He X, Zhuang Y (2017) Social-aware movie recommendation via multimodal network learning. IEEE Trans Multimed 20(2):430–440

    Article  Google Scholar 

  41. 41.

    Pérez-Marcos J, Martín-Gómez L, Jiménez-Bravo DM, López VF, Moreno-García MN (2020) Hybrid system for video game recommendation based on implicit ratings and social networks. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01681-0

    Article  Google Scholar 

  42. 42.

    Katarya R, Verma OP (2018) Recommender system with grey wolf optimizer and FCM. Neural Comput Appl 30(5):1679–1687

    Article  Google Scholar 

  43. 43.

    Ling Z, Xiao Y, Wang H, Xu L, Hsu C-H (2019) Extracting implicit friends from heterogeneous information network for social recommendation. In: Pacific Rim international conference on artificial intelligence. Springer, pp 607–620

  44. 44.

    Barbieri J, Alvim LG, Braida F, Zimbrão G (2017) Autoencoders and recommender systems: COFILS approach. Expert Syst Appl 89:81–90

    Article  Google Scholar 

  45. 45.

    Strub F, Gaudel R, Mary J (2016) Hybrid recommender system based on autoencoders. In: Proceedings of the 1st workshop on deep learning for recommender systems. ACM, pp 11–16

  46. 46.

    Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on world wide web. ACM, pp 111–112

  47. 47.

    Kiran R, Kumar P, Bhasker B (2020) DNNRec: a novel deep learning based hybrid recommender system. Expert Syst Appl 144:113054

    Article  Google Scholar 

  48. 48.

    Gai S, Zhao F, Kang Y, Chen Z, Wang D, Tang A (2019) Deep transfer collaborative filtering for recommender systems. In: Pacific Rim international conference on artificial intelligence. Springer, pp 515–528

  49. 49.

    Dooms S, De Pessemier T, Martens L (2013) Movietweetings: a movie rating dataset collected from twitter. In: Workshop on crowdsourcing and human computation for recommender systems, CrowdRec at RecSys, p 43

  50. 50.

    Polatidis N, Georgiadis CK, Pimenidis E, Mouratidis H (2017) Privacy-preserving collaborative recommendations based on random perturbations. Expert Syst Appl 71:18–25

    Article  Google Scholar 

  51. 51.

    Langseth H, Nielsen TD (2015) Scalable learning of probabilistic latent models for collaborative filtering. Decis Support Syst 74:1–11

    Article  Google Scholar 

  52. 52.

    Elkahky AM, Song Y, He X (2015) A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th international conference on world wide web, pp 278–288

  53. 53.

    Soares M, Viana P (2015) Tuning metadata for better movie content-based recommendation systems. Multimed Tools Appl 74(17):7015–7036

    Article  Google Scholar 

  54. 54.

    Chen H-W, Wu Y-L, Hor M-K, Tang C-Y (2017) Fully content-based movie recommender system with feature extraction using neural network. In: 2017 international conference on machine learning and cybernetics (ICMLC). IEEE, pp 504–509

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Correspondence to Reza Ravanmehr.

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Tahmasebi, H., Ravanmehr, R. & Mohamadrezaei, R. Social movie recommender system based on deep autoencoder network using Twitter data. Neural Comput & Applic (2020). https://doi.org/10.1007/s00521-020-05085-1

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Keywords

  • Social recommender system
  • Deep learning
  • Deep autoencoder network
  • Collaborative filtering
  • Content-based filtering
  • Social influence