Abstract
Recommender system (RS) are a type of suggestion to the information overload problem suffered by user of websites that allow the rating of particular item. The movie RS are one of the most efficient, useful, and widespread applications for individual to watch movie with minimum decision time. Many attempts made by the researchers to solve these issues like watching movie, purchasing book etc., through RS, whereas most of the study fails to address cold start problem, data sparsity and malicious attacks. This study address these problems, we propose trust matrix measure in this paper, which combines user similarity with weighted trust propagation. Non cold user passed through different models with trust filter and a cold user generated an optimal score with their preferences for recommendation. Four different recommendation models such as Backpropagation (BPNN) model, SVD (Singular Value Decomposition) model, DNN (Deep Neural Network model) and DNN with Trust were compared to recommend the suitable movie to the user. Results imply that DNN with trust model proved to be the best model with high accuracy of 83% with 0.74 MSE value and can be used for best movie recommendation.
Similar content being viewed by others
References
Ahirwadkar B, Deshmukh SN (2019) Deep neural networks for recommender systems. Int J Innov Technol Explor 8(12):4838–4842
Al-Shamri MYH, Bharadwaj KK (2008) Fuzzy-genetic approach to recommender systems based on a novel hybrid user model. Expert Syst Appl 35(3):1386–1399
Bedi P, Sharma R (2012) Trust based recommender system using ant colony for trust computation. Expert Syst Appl 39(1):1183–1190
Donovan OJ, Smyth B (2005) Trust in recommender systems. In: Proceedings of the 10th international conference on intelligent user interfaces (IUI’05), San Diego, California, USA, pp 167–174.
Ghodous E, Hamzeh A (2015) A new approach for trust prediction by using collaborative filtering based of pareto dominance in social networks. Ciência e Natura 37(2):95–101
Gohari FS, Aliee FS, Haghighi H (2019) A dynamic local–global trust-aware recommendation approach. Electron Commer Res Appl 34(1):100–108
Gohari FS, Aliee FS, Haghighi H (2020) A significance-based trust-aware recommendation approach. Inf Syst 87(1):101–121
Golbeck J, Rovideskey J (2006) Generating predictive movie recommendations from trust in social networks. In: Proceedings of the 4th international conference on trust management, Pisa, Italy, pp 93–104.
Goldberg D, Nichols D, Okim BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70
Goldberg K, Roeder T, Gupta D, Perkins C (2001) Eigentaste: a constant time collaborative filtering algorithm. Inf Retr 4(2):133–151
Guo G, Zhang J, Smith NY (2016) A novel recommendation model regularized with user trust and item ratings. IEEE Trans Knowl Data Eng 28(7):1607–1620
Gupta S, Nagpal S (2015) An empirical analysis of implicit trust metrics in recommender systems. In: International conference on advances in computing, communication and informatics (ICACCI), Kochi, pp 636–639.
Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR, Rirdl J (1997) GroupLens: applying collaborative filtering to usenet news. Commun ACM 40(3):77–87
Lathia N, Hailes S, Capra L (2008) Trust based collaborative filtering. In: Proceedings of the IFIP international conference on trust management, Trondheim, Norway, pp 119–134.
Massa P, Avesani P (2007) Trust-aware recommender systems. In: Proceedings of recommender systems, ACM, New York, USA, pp 17–24.
Massa P, Bhattacharjee B (2004) Using trust in recommender systems: an experimental analysis. In: International conference on trust management, Oxford, England, pp 221–235.
Mubbashir AM, Ghazanfar MA, Mehmood Z, Alyoubi KH, Alfakeeh AS (2019) Unifying user similarity and social trust to generate powerful recommendations for smart cities using collaborating filtering based recommender systems. Springer, Berlin
Papagelis M, Plexousakis D, Kutsuras T (2005) Alleviating the sparsity problem of collaborative filtering using trust inferences. In: Proceedings of the third international conference on trust management, Berlin, Heidelberg, pp 224–239.
Sun A, Tay Y, Yao L, Zhang S (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 52:1–35
Kuanr M, Mohanty SN (2020) Location-based personalised recommendation systems for the tourists in India. Int J Bus Intell Data Min 7
Kuanr M, Rath BK, Mohanty SN (2018) Crop recommender system for the farmers using mamdani fuzzy inference model. Int. J Eng Technol 7(4.15):277–280
Garanayak M, Mohanty SN, Jagadev AK, Sahoo S (2019) Recommender system using item based collaborative filtering (CF) and K-means. Int J Knowl-based Intell Eng Syst 23(2):93–101
Garanayak M, Sahoo S, Mohanty SN, Jagadev AK (2020) An automated recommender system for educational institute in India. EAI Endorsed Trans Scalable Inf Syst 20(26):1–13
Nagpal S, Arora S, Dey S, Shreya (2017) Feature selection using gravitational search algorithm for biomedical data. Procedia Comput Sci 115:258–265
Ungar LH, Foster DP (1998) Clustering methods for collaborative filtering. In: AAAI workshop on recommendation systems, vol 1, pp 114–129
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Choudhury, S.S., Mohanty, S.N. & Jagadev, A.K. Multimodal trust based recommender system with machine learning approaches for movie recommendation. Int. j. inf. tecnol. 13, 475–482 (2021). https://doi.org/10.1007/s41870-020-00553-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41870-020-00553-2