Recent Developments in Recommender Systems

  • Jia-Ming LowEmail author
  • Ian K. T. Tan
  • Choo-Yee Ting
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11909)


With greater penetration of online services, the use of recommender systems to predict users’ propensity for continuous engagement becomes crucial in ensuring maximum revenue. There are many challenges, such as the cold start problem and data sparsity, that are continuously being addressed by a myriad of techniques in recommender systems. This paper provides insights into the trends of the techniques used for recommender systems and the challenges they address. With the insights; deep learning, matrix factorization or a combination of both can be used in addressing the data sparsity challenge.


  1. 1.
    Alahmadi, D.H., Zeng, X.: Twitter-based recommender system to address cold-start: a genetic algorithm based trust modelling and probabilistic sentiment analysis. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1045–1052, November 2015Google Scholar
  2. 2.
    Banerjee, H., et al.: Movie recommendation system using particle swarm optimization. In: 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), pp. 121–126, August 2017Google Scholar
  3. 3.
    Bobadilla, J., Bojorque, R., Hernando Esteban, A., Hurtado, R.: Recommender systems clustering using bayesian non negative matrix factorization. IEEE Access 6, 3549–3564 (2018)CrossRefGoogle Scholar
  4. 4.
    Chang, S., Abdul, A., Chen, J., Liao, H.: A personalized music recommendation system using convolutional neural networks approach. In: 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 47–49, April 2018Google Scholar
  5. 5.
    Convertini, N., Logrillo, N., Manca, F., Palmisano, T.: Recommendation system using hybrid fuzzy association rules for human smart cities. In: 2018 AEIT International Annual Conference, pp. 1–5, October 2018Google Scholar
  6. 6.
    Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38, 325–339 (1967)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Feng, X., Yu, W., Li, Y.: Faster matrix completion using randomized SVD. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 608–615, November 2018Google Scholar
  8. 8.
    Geng, B., Jiao, L., Gong, M., Li, L., Wu, Y.: A two-step personalized location recommendation based on multi-objective immune algorithm. Inf. Sci. 475, 161–181 (2019)CrossRefGoogle Scholar
  9. 9.
    Gouvert, O., Oberlin, T., Févotte, C.: Negative binomial matrix factorization for recommender systems. CoRR abs/1801.01708 (2018)Google Scholar
  10. 10.
    Guo, G., Zhang, J., Yorke-Smith, N.: Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowl. Based Syst. 74, 14–27 (2015)CrossRefGoogle Scholar
  11. 11.
    Han, C., Lin, B.: A hybrid model of tensor factorization and sentiment utility logistic model for trip recommendation. In: 2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII), pp. 158–161, July 2018Google Scholar
  12. 12.
    Hosseinzadeh Aghdam, M.: Context-aware recommender systems using hierarchical hidden Markov model. Physica A 518, 89–98 (2019)CrossRefGoogle Scholar
  13. 13.
    Jain, S., Grover, A., Thakur, P.S., Choudhary, S.K.: Trends, problems and solutions of recommender system. In: International Conference on Computing, Communication Automation, pp. 955–958, May 2015Google Scholar
  14. 14.
    Jiang, L., Cheng, Y., Yang, L., Li, J., Yan, H., Wang, X.: A trust-based collaborative filtering algorithm for e-commerce recommendation system. J. Ambient Intell. Hum. Comput. (2018)Google Scholar
  15. 15.
    Jiang, M., Yang, Z., Zhao, C.: What to play next? A RNN-based music recommendation system. In: 2017 51st Asilomar Conference on Signals, Systems, and Computers, pp. 356–358, October 2017Google Scholar
  16. 16.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 426–434. ACM, New York (2008)Google Scholar
  17. 17.
    Lee, J., Hwang, W., Parc, J., Lee, Y., Kim, S., Lee, D.: \(l\)-injection: toward effective collaborative filtering using uninteresting items. IEEE Trans. Knowl. Data Eng. 31(1), 3–16 (2019)CrossRefGoogle Scholar
  18. 18.
    Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: Proceedings of the 2005 SIAM International Conference on Data Mining, pp. 471–475. SIAM (2005)Google Scholar
  19. 19.
    Li, G., Zhang, J.: Music personalized recommendation system based on improved KNN algorithm. In: 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 777–781, October 2018Google Scholar
  20. 20.
    Logesh, R., Subramaniyaswamy, V., Malathi, D., Sivaramakrishnan, N., Vijayakumar, V.: Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Comput. Appl. (2018)Google Scholar
  21. 21.
    Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X.Z., Indragandhi, V.: A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Future Gener. Comput. Syst. 83, 653–673 (2018)CrossRefGoogle Scholar
  22. 22.
    Low, Y.H., Yap, W.-S., Tee, Y.K.: Convolutional neural network-based collaborative filtering for recommendation systems. In: Kim, J.-H., Myung, H., Lee, S.-M. (eds.) RiTA 2018. CCIS, vol. 1015, pp. 117–131. Springer, Singapore (2019). Scholar
  23. 23.
    Luo, L., Xie, H., Rao, Y., Wang, F.L.: Personalized recommendation by matrix co-factorization with tags and time information. Expert Syst. Appl. 119, 311–321 (2019)CrossRefGoogle Scholar
  24. 24.
    Mu, Y., Xiao, N., Tang, R., Luo, L., Yin, X.: An efficient similarity measure for collaborative filtering. Procedia Comput. Sci. 147, 416–421 (2019). 2018 International Conference on Identification, Information and Knowledge in the Internet of ThingsCrossRefGoogle Scholar
  25. 25.
    Mudda, S., Lian, D., Giordano, S., Liu, D., Xie, X.: Spatial-aware deep recommender system. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 983–990, October 2018Google Scholar
  26. 26.
    Nabil, S., Elbouhdidi, J., Yassin, M.: Recommendation system based on data analysis-application on tweets sentiment analysis. In: 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), pp. 155–160, October 2018Google Scholar
  27. 27.
    Osadchiy, T., Poliakov, I., Olivier, P., Rowland, M., Foster, E.: Recommender system based on pairwise association rules. Expert Syst. Appl. 115, 535–542 (2019)CrossRefGoogle Scholar
  28. 28.
    Parvin, H., Moradi, P., Esmaeili, S.: TCFACO: trust-aware collaborative filtering method based on ant colony optimization. Expert Syst. Appl. 118, 152–168 (2019)CrossRefGoogle Scholar
  29. 29.
    Pereira Fressato, E., Fortes da Costa, A., Garcia Manzato, M.: Similarity-based matrix factorization for item cold-start in recommender systems. In: 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pp. 342–347, October 2018Google Scholar
  30. 30.
    Preethi, G., Krishna, P.V., Obaidat, M.S., Saritha, V., Yenduri, S.: Application of deep learning to sentiment analysis for recommender system on cloud. In: 2017 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 93–97, July 2017Google Scholar
  31. 31.
    Purkaystha, B., Datta, T., Islam, M.S.: Marium-E-Jannat: rating prediction for recommendation: constructing user profiles and item characteristics using backpropagation. Appl. Soft Comput. 75, 310–322 (2019)CrossRefGoogle Scholar
  32. 32.
    Seo, Y.D., Kim, Y.G., Lee, E., Baik, D.K.: Personalized recommender system based on friendship strength in social network services. Expert Syst. Appl. 69, 135–148 (2017)CrossRefGoogle Scholar
  33. 33.
    Subramaniyaswamy, V., Logesh, R.: Adaptive KNN based recommender system through mining of user preferences. Wireless Pers. Commun. 97(2), 2229–2247 (2017)CrossRefGoogle Scholar
  34. 34.
    Symeonidis, P., Malakoudis, D.: Multi-modal matrix factorization with side information for recommending massive open online courses. Expert Syst. Appl. 118, 261–271 (2019)CrossRefGoogle Scholar
  35. 35.
    Taheri, S.M., Irajian, I.: DeepMovRS: a unified framework for deep learning-based movie recommender systems. In: 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pp. 200–204, February 2018Google Scholar
  36. 36.
    Tahmasbi, H., Jalali, M., Shakeri, H.: Modeling temporal dynamics of user preferences in movie recommendation. In: 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 194–199, October 2018Google Scholar
  37. 37.
    Wang, J., Liu, T.: Improving sentiment rating of movie review comments for recommendation. In: 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), pp. 433–434, June 2017Google Scholar
  38. 38.
    Wang, K., Jin, Y., Wang, H., Peng, H., Wang, X.: Personalized time-aware tag recommendation. In: AAAI (2018)Google Scholar
  39. 39.
    Wang, M., Xiao, Y., Zheng, W., Jiao, X.: RNDM: a random walk method for music recommendation by considering novelty, diversity, and mainstream. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 177–183, November 2018Google Scholar
  40. 40.
    Wang, P., Huang, H., Zhu, J., Qi, L.: A trust-based prediction approach for recommendation system. In: Yang, A., et al. (eds.) SERVICES 2018. LNCS, vol. 10975, pp. 157–164. Springer, Cham (2018). Scholar
  41. 41.
    Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 69, 29–39 (2017)CrossRefGoogle Scholar
  42. 42.
    Xu, A.L., Liu, B.J., Gu, C.Y.: A recommendation system based on extreme gradient boosting classifier. In: 2018 10th International Conference on Modelling, Identification and Control (ICMIC), pp. 1–5, July 2018Google Scholar
  43. 43.
    Xue, J., Zhu, E., Liu, Q., Yin, J.: Group recommendation based on financial social network for robo-advisor. IEEE Access 6, 54527–54535 (2018)CrossRefGoogle Scholar
  44. 44.
    Yang, F., Lu, Y.: Restricted Boltzmann machines for recommender systems with implicit feedback. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 4109–4113, December 2018Google Scholar
  45. 45.
    Yang, W., Fan, S., Wang, H.: An item-diversity-based collaborative filtering algorithm to improve the accuracy of recommender system. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 106–110, October 2018Google Scholar
  46. 46.
    Yi, K., Chen, T., Cong, G.: Library personalized recommendation service method based on improved association rules. Library Hi Tech 36(3), 443–457 (2018)CrossRefGoogle Scholar
  47. 47.
    Yuji, W.: A trust prediction method for recommendation system. In: 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 64–68, August 2017Google Scholar
  48. 48.
    Zhang, B., Zhang, H., Sun, X., Feng, G., He, C.: Integrating an attention mechanism and convolution collaborative filtering for document context-aware rating prediction. IEEE Access 7, 3826–3835 (2019)CrossRefGoogle Scholar
  49. 49.
    Zhang, S., Tay, Y., Yao, L., Sun, A.: Next item recommendation with self-attention. CoRR abs/1808.06414 (2018)Google Scholar
  50. 50.
    Zhang, S., Yao, L., Sun, A.: Deep learning based recommender system: a survey and new perspectives. CoRR abs/1707.07435 (2017)Google Scholar
  51. 51.
    Zhang, W., Liu, F., Jiang, L., Xu, D.: Recommendation based on collaborative filtering by convolution deep learning model based on label weight nearest neighbor. In: 2017 10th International Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp. 504–507, December 2017Google Scholar
  52. 52.
    Zheng, D., Xiong, Y.: A unified probabilistic matrix factorization recommendation algorithm. In: 2018 International Conference on Robots Intelligent System (ICRIS), pp. 246–249, May 2018Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Multimedia UniversityCyberjayaMalaysia
  2. 2.Priority Dynamics Sdn BhdSubang JayaMalaysia
  3. 3.Monash University MalaysiaSubang JayaMalaysia

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