research-article

A Hybrid Deep Ranking Weighted Multi-Hashing Recommender System

Authors:

Jyoti Prakash Singh,

Neha JainAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 23, Issue 8

Article No.: 118, Pages 1 - 11

https://doi.org/10.1145/3626195

Published: 07 August 2024 Publication History

Abstract

In countries where there is a low availability of resources for language, businesses face the challenge of overcoming language barriers to reach their customers. One possible solution is to use collaborative filtering-based recommendation systems in their native languages. These systems employ algorithms that understand the customers’ preferences and suggest products or services in their native language.

Collaborative filtering (CF) is a popular recommendation technique that simulates word-of-mouth phenomena. However, the accuracy of a CF recommendation can be affected by sparse data. In this research article, we present a novel hybrid weighted multi-deep ranking supervised hashing (HWMDRH) approach. Our method leverages both user-based and item-based CF by merging the item-based deep ranking weighted multi-hash recommender system prediction with the user-based deep ranking weighted multi-hash recommender system prediction to generate Top-N prediction. We conducted extensive experiments on the MovieLens 1M dataset, and our results show that the proposed HWMDRH model outperforms existing models and achieves state-of-the-art performance across recall, precision, RMSE, and F1-score metrics.

References

[1]

S. Kant and T. Mahara. 2017. Nearest biclusters collaborative filtering framework with fusion. Journal of Computational Science 25 (2017), 204–212.

[2]

M. Kawasaki and T. Hasuike. 2017. A recommendation system by collaborative filtering including information and characteristics on users and items. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence. IEEE, 1–8.

[3]

D. Das, L. Sahoo, and S. Datta. 2017. A survey on recommendation system. International Journal of Computer Applications 160, 7 (2017), 6–10.

[4]

S. Kant, T. Mahara, V. Jain, D. Jain, and A. Sangaiah. 2017. LeaderRank based k-means clustering initialization method for collaborative filtering. Computers and Electrical Engineering 69 (2017), 598–609.

[5]

P. Lopes and B. Roy. 2015. Dynamic recommendation system using web usage mining for e-commerce users. Procedia Computer Science 45 (2015), 60–69.

[6]

S. Pandya, J. Shah, N. Joshi, H. Ghayvat, S. C. Mukhopadhyay, and M. H. Yap. 2016. A novel hybrid-based recommendation system based on clustering and association mining. In Proceedings of the 2016 10th International Conference on Sensing Technology. IEEE, 1–6.

[7]

J. P. Verma, B. Patel, and A. Patel. 2015. Big data analysis: recommendation system with Hadoop framework. In Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Communication Technology. IEEE, 92–97.

[8]

N. Kushwaha, M. Pant, S. Kant, and V. Jain. 2017. Magnetic optimization algorithm for data clustering. Pattern Recognition Letters 115 (2017), 59–65.

[9]

S. Shaikh, S. Rathi, and P. Janrao. 2017. Recommendation system in E-commerce websites: A Graph Based Approached. In Proceedings of the 2017 IEEE 7th International Advance Computing Conference. IEEE, 931–934.

[10]

T. Rutkowski, J. Romanowski, P. Woldan, P. Staszewski, R. Nielek, and L. Rutkowski. 2018. A content-based recommendation system using neuro-fuzzy approach. In Proceedings of the 2018 IEEE International Conference on Fuzzy Systems. IEEE, 1–8.

Digital Library

[11]

S. Parvatikar and B. Joshi. 2015. Online book recommendation system by using collaborative filtering and association mining. In Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Computing Research. IEEE, 1–4.

[12]

W. T. Chu and Y. L. Tsai. 2017. A hybrid recommendation system considering visual information for predicting favorite restaurants. World Wide Web 20, 6 (2017), 1313–31.

Digital Library

[13]

G. Suganeshwari and S. S. Ibrahim. 2016. A survey on collaborative filtering based recommendation system. In Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges. Springer, Cham, 503–518.

[14]

J. Jooa, S. Bangb, and G. Parka. 2016. Implementation of a recommendation system using association rules and collaborative filtering. Procedia Computer Science 91 (2016), 944–952.

[15]

L. Qi, X. Wang, X. Xu, W. Dou, and S. Li. 2020. Privacy-aware cross-platform service recommendation based on enhanced locality-sensitive hashing. IEEE Transactions on Network Science and Engineering 8, 2 (2020), 1145–1153.

[16]

G. Li, T. Zhu, J. Hua, T. Yuan, Z. Niu, T. Li, and H. Zhang. 2019. Asking images: Hybrid recommendation system for tourist spots by hierarchical sampling statistics and multimodal visual Bayesian personalized ranking. IEEE Access 7 (2019), 126539–126560. DOI:

[17]

M. Weimer, A. Karatzoglou, Q. Le, and A. Smola. 2007. COFIRANK-maximum margin matrix factorization for collaborative ranking. In Proceedings of the Advances in Neural Information Processing Systems, 21st Annual Conference on Neural Information Processing Systems. 222–230.

[18]

S. Kumar, J. P. Singh, V. K. Jain, and A. Marahatta. 2022. A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation. Computational Intelligence and Neuroscience (2022).

Digital Library

[19]

J. Bobadilla, Á. González-Prieto, F. Ortega, and R. Lara-Cabrera. 2022. Deep learning approach to obtain collaborative filtering neighborhoods. Neural Computing and Applications 34, 4 (2022), 2939-2951.

Digital Library

[20]

Y. C. Chen, L. Hui, and T. Thaipisutikul. 2021. A collaborative filtering recommendation system with dynamic time decay. The Journal of Supercomputing 77, 1 (2021), 244–62.

Digital Library

[21]

X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. S. Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. 173–182.

Digital Library

[22]

Z. Abbasi-Moud, H. Vahdat-Nejad, and J. Sadri. 2021. Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Systems with Applications 167 (2021), 114324.

Digital Library

[23]

M. He, W. Pan, and Z. Ming. 2022. BAR: Behavior-aware recommendation for sequential heterogeneous one-class collaborative filtering. Information Sciences 608 (2022), 881–899.

Digital Library

[24]

B. Choe, T. Kang, and K. Jung. 2021. Recommendation system with hierarchical recurrent neural network for long-term time series. IEEE Access 9 (2021), 72033–72039.

[25]

B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk. 2016. Session-based recommendations with recurrent neural networks. 4th International Conference on Learning Representations (ICLR'16), San Juan, Puerto Rico.

[26]

F. M. Harper and J. A. Konstan. 2015. The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems 5, 4 (2015), 1–19.

[27]

J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez. 2013. Recommender systems survey. Knowledge-Based Systems 46 (2013). 109–132.

Digital Library

[28]

Ashish Kumar Tripathi, Himanshu Mittal, Pranav Saxena, and Siddharth Gupta. 2021. A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm. Complex and Intelligent Systems 7 (2021), 297–309. DOI:

[29]

J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal. 2012. A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-based Systems 26 (2012), 225–238.

Cited By

Kumar SSingh JShantanu (2024)Genre-Based Movie Recommender System with XGBoost2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT)10.1109/C3IT60531.2024.10829451(1-6)Online publication date: 28-Sep-2024
https://doi.org/10.1109/C3IT60531.2024.10829451
Kumar SChauhan VUpadhyay DSingh STripathi P(2023)Enhancing Recommender Systems to Alleviate Data Sparsity and the Cold Start Problem2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART)10.1109/SMART59791.2023.10428531(486-491)Online publication date: 22-Dec-2023
https://doi.org/10.1109/SMART59791.2023.10428531

Index Terms

A Hybrid Deep Ranking Weighted Multi-Hashing Recommender System
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 23, Issue 8

August 2024

343 pages

EISSN:2375-4702

DOI:10.1145/3613611

Editor:
Imed Zitouni
Google, USA
,
Guest Editors:
Deepak Kumar Jain,
Thierry Boumans,
Stefano Berretti

Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2024

Online AM: 05 October 2023

Accepted: 18 September 2023

Revised: 28 August 2023

Received: 11 April 2023

Published in TALLIP Volume 23, Issue 8

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Cited By

Kumar SSingh JShantanu (2024)Genre-Based Movie Recommender System with XGBoost2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT)10.1109/C3IT60531.2024.10829451(1-6)Online publication date: 28-Sep-2024
https://doi.org/10.1109/C3IT60531.2024.10829451
Kumar SChauhan VUpadhyay DSingh STripathi P(2023)Enhancing Recommender Systems to Alleviate Data Sparsity and the Cold Start Problem2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART)10.1109/SMART59791.2023.10428531(486-491)Online publication date: 22-Dec-2023
https://doi.org/10.1109/SMART59791.2023.10428531

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