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View all- Huang JWang X(2022)User Experience Evaluation of B2C E-Commerce Websites Based on Fuzzy InformationWireless Communications & Mobile Computing10.1155/2022/67679602022Online publication date: 1-Jan-2022
Research on E-Commerce Platform-Based Personalized Recommendation Algorithm
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Abstract
Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.
References
[1]
Zhang Z., Liu H., Application and research of improved probability matrix factorization techniques in collaborative filtering International Journal of Control & Automation 2014 Volume 7 Issue 8 pp.79 –92
[2]
Bonhard P., Sasse M. A., ‘Knowing me, knowing you’—using profiles and social networking to improve recommender systems BT Technology Journal 2006 Volume 24 Issue 3 pp.84 –98
[3]
Sinha R., Swearingen K., Comparing recommendations made by online systems and friends Proceedings of the Delos-NSF Workshop on Personalization and Rocommonder Systems in Digital Libraries 2001
[4]
Caverlee J., Liu L., Webb S., The SocialTrust framework for trusted social information management: architecture and algorithms Information Sciences 2010 Volume 180 Issue 1 pp.95 –112
[5]
Adomavicius G., Tuzhilin A., Multidimensional recommender systems: a data warehousing approach Electronic Commerce: Second International Workshop, WELCOM 2001 Heidelberg, Germany, November 16-17, 2001 Proceedings 2001 Volume 2232 Berlin, Germany Springer pp.180 –192 Lecture Notes in Computer Science
[6]
Nguyen T. V., Karatzoglou A., Baltrunas L., Gaussian process factorization machines for context-aware recommendations Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR '14 July 2014 Gold Coast, Australia ACM pp.63 –72
[7]
Zhang Y., E-commerce personalized recommendation Advanced Materials Research 2014 Volume 989–994 pp.4996 –4999
[8]
Li Y.-M., Wu C.-T., Lai C.-Y., A social recommender mechanism for e-commerce: combining similarity, trust, and relationship Decision Support Systems 2013 Volume 55 Issue 3 pp.740 –752
[9]
Huang Z., Benyoucef M., From e-commerce to social commerce: a close look at design features Electronic Commerce Research and Applications 2013 Volume 12 Issue 4 pp.246 –259
[10]
Zhang Z., Liu H., Social recommendation model combining trust propagation and sequential behaviors Applied Intelligence 2015 Volume 43 Issue 3 pp.695 –706
[11]
Zhang Z.-J., Liu H., Research on context-awareness mobile SNS recommendation algorithm Pattern Recognition and Artificial Intelligence 2015 Volume 28 Issue 5 pp.404 –410
[12]
Li Y.-M., Chou C.-L., Lin L.-F., A social recommender mechanism for location-based group commerce Information Sciences 2014 Volume 274 pp.125 –142
[13]
Wang J., De Vries A. P., Reinders M. J. T., Unifying user-based and item-based collaborative filtering approaches by similarity fusion Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval August 2006 ACM pp.501 –508
- Research on E-Commerce Platform-Based Personalized Recommendation Algorithm
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Copyright © Copyright © 2016 Zhijun Zhang et al.
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Hindawi Limited
London, United Kingdom
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Published: 01 March 2016
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View all- Huang JWang X(2022)User Experience Evaluation of B2C E-Commerce Websites Based on Fuzzy InformationWireless Communications & Mobile Computing10.1155/2022/67679602022Online publication date: 1-Jan-2022
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