The Differentiation of Residents’ Cultural Consumption Tendency and Consumption Recommendation System Based on Network Inference Algorithm

• Autorzy: Lian Naiyu , Xu Hengzhe , Zhang Feiyang

Identyfikatory

DOI

10.2478/fcds-2024-0008

• Języki publikacji: EN

Abstrakty

EN

To address the issue of insufficient accuracy in consumer recommendation systems, a new biased network inference algorithm is proposed based on traditional network inference algorithms. This new network inference algorithm can significantly improve the resource allocation ability of the original one, thereby improving recommendation performance. Then, the performance of this algorithm is verified through comparative experiments with network-based inference algorithms, network inference algorithms with initial resource optimization, and heterogeneous network inference algorithms. The results showed that the accuracy of the new network inference algorithm was 24.5%, which was superior to traditional one. In terms of system performance testing, the recommendation hit rate of the new network inference algorithm increased by 13.97%, which was superior to the other three comparative algorithms. The experimental results indicated that a novel network inference algorithm with bias can improve the performance of consumer recommendation systems, providing new ideas for improving the performance of consumer recommendation systems.

Słowa kluczowe

EN

recommendation system; network inference algorithm; bias; accuracy

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2024
• Tom: Vol. 49, No. 2
• Strony: 121--138
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Lian Naiyu

• Department of Arts Management, Xinghai Conservatory of Music, Guangzhou, China

autor

Xu Hengzhe

• Faculty of Humanities and Social Sciences City University of Macau, Macau, China

autor

Zhang Feiyang

• Guangzhou South China Academy of Science and Technology of Natural Resources, Guangzhou, China

Bibliografia

• [1] Chen X., Xue Y., Shiue Y. Rule based Semantic Reasoning for Personalized Recommendation in Indoor O2O e-commerce. International Core Journal of Engineering, 2020, 6(1): 309-318.
• [2] Cho Y. J., Yoon K. J. Distance-based camera network topology inference for person re-identification. Pattern recognition letters, 2019, 125: 220-227.
• [3] Fukushima A., Yano T., Imahara S., Aisu H., Shimokawa Y., Shibata Y. Prediction of energy consumption for new electric vehicle models by machine learning. IET Intelligent Transport Systems, 2018, 12(9): 1174-1180.
• [4] Geukens F., Maes M., Spithoven A., Pouwels J. L., Danneel S., Cillessen A. H., Goossens L. Changes in adolescent loneliness and concomitant changes in fear of negative evaluation and self-esteem:. International Journal of Behavioral Development, 2022, 46(1): 10-17.
• [5] Han X., Wang Z., Xu H. J. Time-Weighted Collaborative Filtering Algorithm Based on Improved Mini Batch K-Means Clustering. Advances in Science and Technology, 2021, 105: 309-317.
• [6] Ivek I., Borgsted C., Pedersen S. T., Pinborg A. B., Oranje B., Frokjaer V. G. P.0282 Evidence for steroid hormone associations with key brain markers of information filtering in healthy women. European Neuropsychopharmacology, 2021, 53: 203-204.
• [7] Jayashree R., Christy A. Recommended system for enhancing tag popularity in a question answering community through splaying techniques. Journal of Advanced Research in Dynamical and Control Systems, 2018, 10(6): 925-933.
• [8] Kumar M., Reddy M. R. A C4.5 Decision Tree Algorithm with MRMR Features Selection Based recommendation system for Tourists. Psychology (Savannah, Ga.), 2021, 58(1): 3640-3643.
• [9] Liu D., Huo C., Yan H. Research of commodity recommendation workflow based on LSH algorithm. Multimedia Tools & Applications, 2019, 78(4): 4327-4345.
• [10] Liu Y., Huang L. Z., Liu Y., Xu T., Chen E. H. Exploiting Structural and Temporal Influence for Dynamic Social-Aware Recommendation. Journal of Computer Science and Technology, 2020, 35(2): 281-294.
• [11] Saba W. E., Beheiry S. M., Abu-Lebdeh G., AL-Tekreeti M. S. A Holistic Intersection Rating System (HIRS)-A Novel Methodology to Measure the Holistic Operational Performance of Signalized Urban Intersections. Smart Cities, 2021, 4(3): 1018-1038.
• [12] Shi X., Wang K., Han X. Method for Determining Fault Sample Size Based on Hierarchical Bayesian Network and Posterior Risk Criteria. Binggong Xuebao/Acta Armamentarii, 2019, 40(1): 171-181.
• [13] Tao J. A PSpace Algorithm for Acyclic Epistemic DL ALCS5_m. Journal of Automated Reasoning, 2019, 63(3): 539-555.
• [14] Trinh H. C., Kwon Y. K. A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data. Bioinformatics, 2021, 37(1): 383-391.
• [15] Turabieh H., Mafarja M., Mirjalili S. Dynamic Adaptive Network-Based Fuzzy Inference System (D-ANFIS) for the imputation of missing data for Internet Medical Things Applications. IEEE Internet of Things Journal,2019, 6(6): 9316-9325.
• [16] Wang D., Chen Y. A Novel Many-Objective Recommendation Algorithm for Multistakeholders. IEEE Access, 2020, 8: 196482-196499.
• [17] Xu H. A recommendation system based on user’s Content Consumption Patterns and Objects of the Interest. Advances in computational sciences and technology, 2018, 11(3): 181-190.
• [18] Ye J., Fei G., Zhai X., Hu G. Network Topology Inference Based on Subset Structure Fusion. IEEE Access, 2020, 8: 194192-194205.
• [19] Zheng G., Yu H., Xu W. Collaborative Filtering Recommendation Algorithm with Item Label Features. International Core Journal of Engineering, 2020, 6(1): 160-170.
• [20] Zheng K., Yang X., Wang Y., Wu Y., Zheng X. Collaborative filtering recommendation algorithm based on variational inference. International Journal of Crowd Science, 2020, 4(1): 31-44.