A Context-Aware Recommendation System with Effective Contextual Pre-Filtering Model

Duaa H. Hameed, Rehab F. Hassan

Abstract


Informational resources have significantly expanded as a result of the growth of the internet. Consequently, making personalized suggestions about different types of information, goods, and services is the best strategy to assist customers in solving the issue of information overload. As a result, recommendation systems are employed to aid clients in locating the products most appropriate to their interests. The majority of traditional recommender systems rely on a traditional model that just takes into account user-item-rating interactions without taking context into account. It has been demonstrated that context-aware recommender systems deliver improved predicted performance across a variety of areas by attempting to adapt to users' preferences across various settings. This study presents a proposed system to help the recommender system solve its difficulties in producing accurate predictions that are relevant to the user's preferences. The system is the Contextual Pre-filtering Based Collaborative Filtering (CPBCF) model, which is based on splitting items. To decrease the time and space needed for processing correlations, it depends on the recommended splitting approach utilizing the variance equation, which decreases the dataset depending on the most important attributes. In the proposed system experiments, the performance of CPBCF with and without contextual pre-filtering was enhanced by (5-7%) for the precision, (7-8%) for the recall, and (7-8%) for the f1-measuer. While the complexity time has enhanced by (3-4 sec). The effectiveness of the CPBCF model was evaluated using various numbers of neighbors. We can observe that neighborhood size does have an effect on forecast accuracy.


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References


N. F. AL-Bakri and S. H. Hashim, “Collaborative Filtering Recommendation Model Based on k-means Clustering,” Al-Nahrain J. Sci., vol. 22, no. 1, pp. 74–79, 2019, doi: 10.22401/anjs.22.1.10.

A. H. N. Rafsanjani, N. Salim, A. R. Aghdam, and K. B. Fard, “Recommendation Systems : a review,” Int. J. Comput. Eng. Res., vol. 3, no. 5, pp. 47–52, 2013.

F. T. A. Hussien, A. M. S. Rahma, and H. B. Abdulwahab, “An E‐Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior,” Sustain., vol. 13, no. 19, pp. 1–21, 2021, doi: 10.3390/su131910786.

J. Anitha and M. Kalaiarasu, “Optimized machine learning based collaborative filtering (OMLCF) recommendation system in e-commerce,” J. Ambient Intell. Humaniz. Comput., vol. 12, pp. 6387–6398, 2021, doi: 10.1007/s12652-020-02234-1.

R. Sharma, D. Gopalani, and Y. Meena, “Collaborative Filtering-Based Recommender System: Approaches and Research Challenges,” in 3rd International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1–6, 2017.

Z. Huang, D. Zeng, and H. Chen, “A Comparative Study of Recommendation Algorithms in E-Commerce Applications,” IEEE Intell. Syst., vol. 22, no. 5, pp. 68–78, 2007.

S. Raza and C. Ding, “Progress in context-aware recommender systems - An overview,” Comput. Sci. Rev., vol. 31, pp. 84–97, 2019, doi: 10.1016/j.cosrev.2019.01.001.

L. Baltrunas and F. Ricci, “Experimental evaluation of context-dependent collaborative filtering using item splitting,” User Model. User-adapt. Interact., vol. 24, pp. 7–34, 2014, doi: 10.1007/s11257-012-9137-9.

Y. Zhang and L. Wang, “Some Challenges for Context-aware Recommender Systems,” 5th Int. Conf. Comput. Sci. Educ., pp. 362–365, 2010.

K. V. Rodpysh, S. J. Mirabedini, and T. Banirostam, “Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems,” Electron. Commer. Res., pp. 1–27, 2021, doi: 10.1007/s10660-021-09488-7.

Y. Zheng, “Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison,” Information, vol. 13, no. 1, pp. 1–18, 2022, doi: 10.3390/info13010042.

G. Adomavicius and A. Tuzhilin, “Context-Aware Recommender Systems,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Boston, MA: Springer US, pp. 217–253, 2010, doi: 10.1007/978-0-387-85820-3_7.

A. Bozanta and B. Kutlu, “Developing a Contextually Personalized Hybrid Recommender System,” Mob. Inf. Syst., pp. 1–13, 2018, doi: 10.1155/2018/3258916.

M. Singh, H. Sahu, and N. Sharma, “A Personalized Context-Aware Recommender System Based on User-Item Preferences,” in InData Management, Analytics and Innovation: Proceedings of ICDMAI, vol. 2, pp. 357–374, 2019, doi: 10.1007/978-981-13-1274-8_28.

J. Silva et al., “Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profile,” Adv. Neural Networks, pp. 200–209, 2019, doi: 10.1007/978-3-030-22808-8_21.

I. M. Al Jawarneh et al., “A Pre-Filtering Approach for Incorporating Contextual Information Into Deep Learning Based Recommender Systems,” IEEE Access, vol. 8, pp. 40485–40498, 2020, doi: 10.1109/ACCESS.2020.2975167.

Z. El Yebdri, S. M. Benslimane, F. Lahfa, M. Barhamgi, and D. Benslimane, “Context-aware recommender system using trust network,” Computing, vol. 103, no. 9, pp. 1919–1937, 2021, doi: 10.1007/s00607-020-00876-9.

Q. Li and B. M. Kim, “Clustering Approach for Hybrid Recommender System,” IEEE/WIC Int. Conf. Web Intell., pp. 33–38, 2003.

R. G. Lumauag, A. M. Sison, and R. P. Medina, “An Enhanced Recommendation Algorithm Based on Modified User-Based Collaborative Filtering,” in 4th International Conference on Computer and Communication Systems (ICCCS), pp. 198–202, 2019.

T. Li, Y. Li, and C. Y. Phoebe, “Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction,” Sci. Program., pp. 1–14, 2021, doi: 10.1155/2021/2592604.

S. G. K. Patro, B. K. Mishra, S. K. Panda, R. Kumar, and H. V. Long, “Knowledge-based preference learning model for recommender system using adaptive neuro-fuzzy inference system,” J. Intell. Fuzzy Syst., vol. 39, no. 3, pp. 4651–4665, 2020, doi: 10.3233/JIFS-200595.

X. Bai, M. Wang, I. Lee, Z. Yang, X. Kong, and F. Xia, “Scientific Paper Recommendation: A Survey,” IEEE Access, vol. 7, pp. 9324–9339, 2019, doi: 10.1109/ACCESS.2018.2890388.

C. K. Raghavendra and K. C. Srikantaiah, “Similarity Based Collaborative Filtering Model for Movie Recommendation Systems,” 5th Int. Conf. Intell. Comput. Control Syst. ICICCS, pp. 1143–1147, 2021.

Rohit, S. Sabitha, and T. Choudhury, “Proposed Approach for Book Recommendation Based on User k-NN,” Ina. Comput. Comput. Sci. Proc. ICCCC, vol. 2, pp. 543–558, 2018, doi: 10.1007/978-981-10-3773-3_53.

S. Chakraborty, M. S. Hoque, N. R. Jeem, M. C. Biswas, D. Bardhan, and E. Lobaton, “Fashion Recommendation Systems, Models and Methods: A Review,” Informatics, vol. 8, no. 3, pp. 1–34, 2021, doi: 10.3390/informatics8030049.

M. Schedl, H. Zamani, C. W. Chen, Y. Deldjoo, and M. Elahi, “Current challenges and visions in music recommender systems research,” Int. J. Multimed. Inf. Retr., vol. 7, pp. 95–116, 2018, doi: 10.1007/s13735-018-0154-2.

N. F. Al-Bakri and S. H. Hashim, “Reducing Data Sparsity in Recommender Systems,” J. Al-Nahrain Univ. Sci., vol. 21, no. 2, pp. 138–147, 2018, doi:10.22401/jnus.21.2.20.




DOI: https://doi.org/10.31449/inf.v49i15.5065

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