Recommender system is an encouraging technology for enterprises to present personalized suggestions to their customers. But this technology suffers from sparsity problem. In addition, greatest researches are grounded on explicit rating. But most users do not spend time for rating of products. Therefore, this research proposes an effective recommendation based on user behavior Consumer behavior is one of the most important issues that have been discussed in recent decades. Organizations always want to understand how consumer makes decisions so that they can use it to design their products and services. Having a correct understanding of the consumers and the consumption process has many advantages. These advantages include helping managers make decisions, providing a cognitive basis through consumer analysis, helping legislators and regulators legislate on the purchase and sale of goods and services, and ultimately helping consumers make better decisions. Here is a solution for recommending goods based on the users’ past behavior over deep learning. The architecture expressed for deep learning is trained by users’ past behavioral data. Amazon data was studied and the results indicated that the proposed method has a much higher accuracy than similar methods. Primary contribution is implementation of a user behavior-based recommendation method that discovers interest of users based on implicit rating of product attributes. In addition, this approach uses sequential pattern of purchasing to improve the quality of recommendation.
| Published in | American Journal of Embedded Systems and Applications (Volume 8, Issue 1) |
| DOI | 10.11648/j.ajesa.20210801.11 |
| Page(s) | 1-6 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Recommendation Systems Deep Learning Users’ Behavioral Afan Oromo
| [1] | De Pessemier, T., Leroux, S., Vanhecke, K., Martens, L.: Combining collaborative filtering and search engine into hybrid news recommendation. Universiteit Gent (2015). |
| [2] | Ricci, F., et al. (eds.): Recommender Systems Handbook. Springer, New York (2015). https://doi.org/10.1007/978-1-4899-7637-6. |
| [3] | Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7 (1), 76–80 (2013). |
| [4] | Yun, S.-Y., Youn, S.-D.: Recommender system based on user information. IEEE (2011). |
| [5] | Drachsler, H., Hummel, H., Koper, R.: Recommendations for learners are different: applying memory based recommender system techniques to lifelong learning (2007). |
| [6] | Halder, S., Sarkar, A. M. J., Lee, Y.-K.: Movie recommendation system based on movie swarm. In: 2012 Second International Conference Cloud and Green Computing (CGC) (2012). |
| [7] | Zhou, G., Zhao, J., He, T., Wu, W.: An empirical study of topic-sensitive probabilistic model for expert finding in question answer communities (2014). |
| [8] | Tung, Y.-H., Tseng, S.-S., Weng, J.-F., Lee, T.-P., Liao, A. Y. H., Tsai, W.-N.: A rule-based CBR approach for expert finding and problem diagnosis (2009). |
| [9] | Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation (1997). |
| [10] | Li, Y. M., Wu, C. T., Lai, C. Y.: A social recommender mechanism for e-commerce: combining similarity, trust, and relationship. Decis. Support Syst. 55, 740–752 (2013). |
| [11] | Lee, J.-H., Yuan, X., Kim, S.-J., Kim, Y.-H.: Toward a user-oriented recommendation system for real estate websites. Inf. Syst. 38 (2), 231–243 (2013). |
| [12] | Scholz, M., Dorner, V., Schryenc, G., Benlian, A.: A configuration-based recommender system for supporting e-commerce decisions. Eur. J. Oper. Res. 259 (1), 205–215 (2017). |
| [13] | Mojtaba Salehi an effective recommendation based on user behavior: a hybrid of sequential pattern of user and attributes of product. In 2013 pp 1-18 |
| [14] | Tariku in 2017 etd.aau.edu.et http://etd.aau.edu.et/bitstream/handle/123456789/12694/Chaltu%20Fita%20Elanso.pdf;jsessionid=4B17F399F72B0CD3C9C4D8A8FA4DFDC0?sequence=1 |
| [15] | Tilahun Gamta, 1992 (etd.aau.edu.et). |
APA Style
Kedir Lemma Arega, Fanta Teferi Megersa. (2021). Smart Recommendation System Based on Understanding User Behaviour for Afan Oromo Language with Deep Learning. American Journal of Embedded Systems and Applications, 8(1), 1-6. https://doi.org/10.11648/j.ajesa.20210801.11
ACS Style
Kedir Lemma Arega; Fanta Teferi Megersa. Smart Recommendation System Based on Understanding User Behaviour for Afan Oromo Language with Deep Learning. Am. J. Embed. Syst. Appl. 2021, 8(1), 1-6. doi: 10.11648/j.ajesa.20210801.11
AMA Style
Kedir Lemma Arega, Fanta Teferi Megersa. Smart Recommendation System Based on Understanding User Behaviour for Afan Oromo Language with Deep Learning. Am J Embed Syst Appl. 2021;8(1):1-6. doi: 10.11648/j.ajesa.20210801.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ajesa.20210801.11,
author = {Kedir Lemma Arega and Fanta Teferi Megersa},
title = {Smart Recommendation System Based on Understanding User Behaviour for Afan Oromo Language with Deep Learning},
journal = {American Journal of Embedded Systems and Applications},
volume = {8},
number = {1},
pages = {1-6},
doi = {10.11648/j.ajesa.20210801.11},
url = {https://doi.org/10.11648/j.ajesa.20210801.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20210801.11},
abstract = {Recommender system is an encouraging technology for enterprises to present personalized suggestions to their customers. But this technology suffers from sparsity problem. In addition, greatest researches are grounded on explicit rating. But most users do not spend time for rating of products. Therefore, this research proposes an effective recommendation based on user behavior Consumer behavior is one of the most important issues that have been discussed in recent decades. Organizations always want to understand how consumer makes decisions so that they can use it to design their products and services. Having a correct understanding of the consumers and the consumption process has many advantages. These advantages include helping managers make decisions, providing a cognitive basis through consumer analysis, helping legislators and regulators legislate on the purchase and sale of goods and services, and ultimately helping consumers make better decisions. Here is a solution for recommending goods based on the users’ past behavior over deep learning. The architecture expressed for deep learning is trained by users’ past behavioral data. Amazon data was studied and the results indicated that the proposed method has a much higher accuracy than similar methods. Primary contribution is implementation of a user behavior-based recommendation method that discovers interest of users based on implicit rating of product attributes. In addition, this approach uses sequential pattern of purchasing to improve the quality of recommendation.},
year = {2021}
}
TY - JOUR T1 - Smart Recommendation System Based on Understanding User Behaviour for Afan Oromo Language with Deep Learning AU - Kedir Lemma Arega AU - Fanta Teferi Megersa Y1 - 2021/01/04 PY - 2021 N1 - https://doi.org/10.11648/j.ajesa.20210801.11 DO - 10.11648/j.ajesa.20210801.11 T2 - American Journal of Embedded Systems and Applications JF - American Journal of Embedded Systems and Applications JO - American Journal of Embedded Systems and Applications SP - 1 EP - 6 PB - Science Publishing Group SN - 2376-6085 UR - https://doi.org/10.11648/j.ajesa.20210801.11 AB - Recommender system is an encouraging technology for enterprises to present personalized suggestions to their customers. But this technology suffers from sparsity problem. In addition, greatest researches are grounded on explicit rating. But most users do not spend time for rating of products. Therefore, this research proposes an effective recommendation based on user behavior Consumer behavior is one of the most important issues that have been discussed in recent decades. Organizations always want to understand how consumer makes decisions so that they can use it to design their products and services. Having a correct understanding of the consumers and the consumption process has many advantages. These advantages include helping managers make decisions, providing a cognitive basis through consumer analysis, helping legislators and regulators legislate on the purchase and sale of goods and services, and ultimately helping consumers make better decisions. Here is a solution for recommending goods based on the users’ past behavior over deep learning. The architecture expressed for deep learning is trained by users’ past behavioral data. Amazon data was studied and the results indicated that the proposed method has a much higher accuracy than similar methods. Primary contribution is implementation of a user behavior-based recommendation method that discovers interest of users based on implicit rating of product attributes. In addition, this approach uses sequential pattern of purchasing to improve the quality of recommendation. VL - 8 IS - 1 ER -
Information
Email: