State-of-the-Art in Multi-faceted Feature Matching for E-Commerce: A Comprehensive Analysis
Abstract
In this paper we provided an insightful exploration into the critical role of feature matching in enhancing the efficacy of e-commerce recommendation systems. By meticulously analyzing user data and product characteristics, feature matching significantly improves the personalization of product suggestions, thereby augmenting user experience and engagement in online shopping platforms. The paper reviews existing methodologies, including collaborative filtering, content-based filtering, hybrid models, and advanced deep learning approaches, which have collectively advanced the domain of personalized recommendations. Despite these advancements, the paper identifies key limitations such as scalability challenges, the cold start problem, over-specialization versus diversity in recommendations, and concerns over user privacy and data security. Through this comprehensive analysis, the survey makes substantial contributions by delineating the current state of the art, pinpointing critical gaps, and suggesting avenues for future research. The paper particularly emphasizes the need for dynamic adaptation in recommendation systems to keep pace with the fast-evolving e-commerce sector and changing consumer behaviors. Future research directions highlighted include the exploration of unsupervised and reinforcement learning models, the development of cross-domain recommendation systems, and the integration of emerging technologies, all aimed at fostering more nuanced, adaptable, and ethically responsible recommendation mechanismsReferences
Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101.
Sohl, T., Vroom, G., & McCann, B. T. (2020). Business model diversification and firm performance: A demand‐side perspective. Strategic entrepreneurship journal, 14(2), 198-223.
Elahi, M., Kholgh, D. K., Kiarostami, M. S., Oussalah, M., & Saghari, S. (2023). Hybrid recommendation by incorporating the sentiment of product reviews. Information Sciences, 625, 738-756.
Guo, Q., Zhuang, F., Qin, C., Zhu, H., Xie, X., Xiong, H., & He, Q. (2020). A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering, 34(8), 3549-3568.
Chen, C. Y., & Huang, J. J. (2023). Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems. Future Internet, 15(10), 323.
Ma, R., Liu, N., Yuan, J., Yang, H., & Zhang, J. (2022, September). Caen: A hierarchically attentive evolution network for item-attribute-change-aware recommendation in the growing e-commerce environment. In Proceedings of the 16th ACM Conference on Recommender Systems (pp. 278-287).
Alamdari, P. M., Navimipour, N. J., Hosseinzadeh, M., Safaei, A. A., & Darwesh, A. (2020). A systematic study on the recommender systems in the E-commerce. Ieee Access, 8, 115694-115716.
Chen, Z., Cao, H., Xu, F., Cheng, M., Wang, T., & Li, Y. (2020). Understanding the role of intermediaries in online social e-commerce: an exploratory study of beidian. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1-24.
Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electronic markets, 32(1), 297-338.
Ahmad, A. Y. B., Gongada, T. N., Shrivastava, G., Gabbi, R. S., Islam, S., & Nagaraju, K. (2023). E-commerce trend analysis and management for Industry 5.0 using user data analysis. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 135-150.
Fink, A., & Elisabetta, E. (2019). Skill flows and the Fourth Industrial Revolution: future questions and directions for the ASEAN Economic Community. In Skilled Labor Mobility and Migration (pp. 267-292). Edward Elgar Publishing.
Schedl, M., Zamani, H., Chen, C. W., Deldjoo, Y., & Elahi, M. (2018). Current challenges and visions in music recommender systems research. International Journal of Multimedia Information Retrieval, 7, 95-116.
Liu, H., Huang, Y., Wang, Z., Liu, K., Hu, X., & Wang, W. (2019). Personality or value: A comparative study of psychographic segmentation based on an online review enhanced recommender system. Applied Sciences, 9(10), 1992.
Wang, C., Zheng, Y., Jiang, J., & Ren, K. (2018). Toward privacy-preserving personalized recommendation services. Engineering, 4(1), 21-28.
Iftikhar, A., Ghazanfar, M. A., Ayub, M., Mehmood, Z., & Maqsood, M. (2020). An improved product recommendation method for collaborative filtering. IEEE Access, 8, 123841-123857.
Deldjoo, Y., Dacrema, M. F., Constantin, M. G., Eghbal-Zadeh, H., Cereda, S., Schedl, M., ... & Cremonesi, P. (2019). Movie genome: alleviating new item cold start in movie recommendation. User Modeling and User-Adapted Interaction, 29, 291-343.
Zohaib, M. (2018). Dynamic difficulty adjustment (DDA) in computer games: A review. Advances in Human-Computer Interaction, 2018.
Ma, Y., Narayanaswamy, B., Lin, H., & Ding, H. (2020, August). Temporal-contextual recommendation in real-time. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2291-2299).
Deldjoo, Y., Dacrema, M. F., Constantin, M. G., Eghbal-Zadeh, H., Cereda, S., Schedl, M., ... & Cremonesi, P. (2019). Movie genome: alleviating new item cold start in movie recommendation. User Modeling and User-Adapted Interaction, 29, 291-343.
Hoofnagle, C. J., Van Der Sloot, B., & Borgesius, F. Z. (2019). The European Union general data protection regulation: what it is and what it means. Information & Communications Technology Law, 28(1), 65-98.
Ma, Y., Narayanaswamy, B., Lin, H., & Ding, H. (2020, August). Temporal-contextual recommendation in real-time. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2291-2299).
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https://doi.org/10.31449/inf.v48i12.6007Downloads
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