{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T19:54:13Z","timestamp":1771876453528,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>In certain newly established or niche e-commerce platforms, user\u2013item interactions are often exceedingly sparse due to limited user bases or specialized product lines, posing significant obstacles to accurate personalized recommendations. To address these challenges, this paper proposes an enhanced recommendation approach based on a latent factor model. By leveraging factorization to uncover the hidden features of users and items and incorporating both user behavioral data and item attribute information, a multi-dimensional latent semantic space is constructed to more effectively capture the underlying relationships between user preferences and item properties. The method involves data preprocessing, model construction, user and item vectorization, and semantic-similarity-based recommendation generation. For empirical validation, we employ a real-world dataset gathered from an e-commerce platform, comprising 4645 ratings from 3445 users across 277 items in nine distinct categories. Experimental results demonstrate that, compared with conventional collaborative filtering methods, this approach achieves superior precision and recall even in highly sparse settings, showing stronger resilience under low-density conditions. These findings offer objective and feasible insights for advancing personalized recommendation techniques in newly established or niche e-commerce platforms.<\/jats:p>","DOI":"10.3390\/systems13050372","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T06:40:23Z","timestamp":1747118423000},"page":"372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Enhanced Latent Factor Recommendation Approach for Sparse Datasets of E-Commerce Platforms"],"prefix":"10.3390","volume":"13","author":[{"given":"Wenbin","family":"Wu","sequence":"first","affiliation":[{"name":"The Faculty of Education, Shaanxi Normal University, Xi\u2019an 710063, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6777-9536","authenticated-orcid":false,"given":"Zhanyong","family":"Qi","sequence":"additional","affiliation":[{"name":"The Faculty of Education, Shaanxi Normal University, Xi\u2019an 710063, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6398-7461","authenticated-orcid":false,"given":"Jiawei","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hanyang University, Ansan 15577, Republic of Korea"}]},{"given":"Bixi","family":"Wang","sequence":"additional","affiliation":[{"name":"Admissions and Employment Office, Xi\u2019an University, Xi\u2019an 710065, China"}]},{"given":"Minyi","family":"Tang","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, ESIGELEC, Av. Galil\u00e9e, 76801 Saint-\u00c9tienne-du-Rouvray, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5599-2607","authenticated-orcid":false,"given":"Xuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Public Administration, University of Electronic Science and Technology of China, Chengdu 610054, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.ijar.2022.08.015","article-title":"A fuzzy content-based group recommender system with dynamic selection of the aggregation functions","volume":"150","author":"Yera","year":"2022","journal-title":"Int. J. Approx. Reason."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1007\/s11257-022-09354-7","article-title":"Use of topical and temporal profiles and their hybridisation for content-based recommendation","volume":"33","author":"Huete","year":"2023","journal-title":"User Model. User-Adapt. Interact."},{"key":"ref_3","first-page":"1","article-title":"A Study on the Development of the School Library Book Recommendation System Using the Association Rule","volume":"39","author":"Jeonghoon","year":"2022","journal-title":"J. Korean Soc. Inf. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Venkatesan, V.K., Ramakrishna, M.T., Batyuk, A., Barna, A., and Havrysh, B. (2023). High-Performance Artificial Intelligence Recommendation of Quality Research Papers Using Effective Collaborative Approach. Systems, 11.","DOI":"10.3390\/systems11020081"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chun, J., Lin, T., and Hong, S. (2022, January 25\u201327). Research on cross-domain recommendation algorithm based on quadratic collaborative filtering. Proceedings of the IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), Changchun, China.","DOI":"10.1109\/EEBDA53927.2022.9744969"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., and Shapira, B. (2022). Advances in Collaborative Filtering. Recommender Systems Handbook, Springer.","DOI":"10.1007\/978-1-0716-2197-4"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10115-021-01628-7","article-title":"Collaborative filtering recommender systems taxonomy","volume":"64","author":"Papadakis","year":"2022","journal-title":"Knowl. Inf. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ramakrishna, M.T., Venkatesan, V.K., Bhardwaj, R., Bhatia, S., Rahmani, M.K.I., Lashari, S.A., and Alabdali, A.M. (2023). HCoF: Hybrid Collaborative Filtering Using Social and Semantic Suggestions for Friend Recommendation. Electronics, 12.","DOI":"10.3390\/electronics12061365"},{"key":"ref_9","first-page":"4544152","article-title":"Collaborative Filtering Recommendation Algorithm Based on User Attributes and Item Score","volume":"2022","author":"Liu","year":"2022","journal-title":"Sci. Program."},{"key":"ref_10","first-page":"8693","article-title":"Multimodal deep collaborative filtering recommendation based on dual attention","volume":"35","author":"Yin","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xia, J., Li, D., Gu, H., Lu, T., Zhang, P., and Gu, N. (2021, January 1\u20135). Incremental Graph Convolutional Network for Collaborative Filtering. Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), Virtual Event, QLD, Australia.","DOI":"10.1145\/3459637.3482354"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108548","DOI":"10.1016\/j.asoc.2022.108548","article-title":"Neural collaborative filtering with multicriteria evaluation data","volume":"119","author":"Morise","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, J., Qi, S., Chen, L., and Yan, H. (2021). Research on personalized recommendation based on big data technology. The 10th International Conference on Computer Engineering and Networks, Springer.","DOI":"10.1007\/978-981-15-8462-6_28"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s44196-023-00299-2","article-title":"Boosting the Item-Based Collaborative Filtering Model with Novel Similarity Measures","volume":"16","author":"Abdalla","year":"2023","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, D., Zheng, Y., Liu, Z., Zheng, W., Tian, J., and Fan, X. (2021, January 19\u201320). Personalized Recommendation System of Innovation and Entrepreneurship Course Based on Collaborative Filtering. Proceedings of the 2021 International Conference on Networking Systems of AI (INSAI), Shanghai, China.","DOI":"10.1109\/INSAI54028.2021.00016"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11334-020-00372-5","article-title":"Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: Benefit for the society","volume":"17","author":"Bandyopadhyay","year":"2021","journal-title":"Innov. Syst. Softw. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"113748","DOI":"10.1016\/j.dss.2022.113748","article-title":"Combining review-based collaborative filtering and matrix factorization: A solution to rating\u2019s sparsity problem","volume":"156","author":"Duan","year":"2022","journal-title":"Decis. Support Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3473","DOI":"10.1007\/s10462-020-09928-0","article-title":"Various dimension reduction techniques for high dimensional data analysis: A review","volume":"54","author":"Ray","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1007\/s40747-021-00637-x","article-title":"Feature dimensionality reduction: A review","volume":"8","author":"Jia","year":"2022","journal-title":"Complex & Intelligent Systems"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"22123","DOI":"10.1109\/JIOT.2021.3086845","article-title":"Deep Neural Network Security Collaborative Filtering Scheme for Service Recommendation in Intelligent Cyber-Physical Systems","volume":"9","author":"Liang","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_21","first-page":"2520140","article-title":"Personalized Recommendation Algorithm of Tourist Attractions Based on Transfer Learning","volume":"2022","author":"Liu","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2001, January 1\u20135). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th international conference on World Wide Web, Hong Kong.","DOI":"10.1145\/371920.372071"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3023","DOI":"10.1007\/s12652-018-0928-7","article-title":"A trust-based collaborative filtering algorithm for E-commerce recommendation system","volume":"10","author":"Jiang","year":"2019","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s40747-019-00123-5","article-title":"Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering","volume":"6","author":"Chen","year":"2020","journal-title":"Complex Intell. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/TCYB.2018.2795041","article-title":"A Novel Deep Learning-Based Collaborative Filtering Model for Recommendation System","volume":"49","author":"Fu","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4463","DOI":"10.1007\/s10115-024-02094-7","article-title":"An academic recommender system on large citation data based on clustering, graph modeling and deep learning","volume":"66","author":"Stergiopoulos","year":"2024","journal-title":"Knowl. Inf. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"196914","DOI":"10.1109\/ACCESS.2024.3517492","article-title":"Contemporary Recommendation Systems on Big Data and Their Applications: A Survey","volume":"12","author":"Xia","year":"2024","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.ins.2020.02.052","article-title":"A fusion collaborative filtering method for sparse data in recommender systems","volume":"521","author":"Feng","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Figuera, P., and Garc\u00eda Bringas, P. (2024). Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and Insights. Technologies, 12.","DOI":"10.3390\/technologies12010005"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e12647","DOI":"10.1111\/exsy.12647","article-title":"Deep learning techniques for recommender systems based on collaborative filtering","volume":"37","author":"Martins","year":"2020","journal-title":"Expert Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wilhelm, F., Mohr, M., and Michiels, L. (2022). An Interpretable Model for Collaborative Filtering Using an Extended Latent Dirichlet Allocation Approach. Int. FLAIRS Conf. Proc., 35.","DOI":"10.32473\/flairs.v35i.130567"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"16004","DOI":"10.1007\/s10489-021-03143-2","article-title":"Latent semantic-enhanced discrete hashing for cross-modal retrieval","volume":"52","author":"Liu","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Klymash, M., Hordiichuk-Bublivska, O., Pyrih, Y., and Urikova, O. (2024). A Hybrid Collaborative Filtering Based Recommender Model Using Modified Funk SVD Algorithm. Digital Ecosystems: Interconnecting Advanced Networks with AI Applications, Springer.","DOI":"10.1007\/978-3-031-61221-3_12"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"26002","DOI":"10.1109\/ACCESS.2022.3156969","article-title":"Parallel Algorithm of Improved FunkSVD Based on GPU","volume":"10","author":"Xiaochen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3845","DOI":"10.1109\/TNNLS.2022.3200009","article-title":"A Prediction-Sampling-Based Multilayer-Structured Latent Factor Model for Accurate Representation to High-Dimensional and Sparse Data","volume":"35","author":"Wu","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_36","first-page":"609","article-title":"Root mean square error or mean absolute error?","volume":"585","author":"Karunasingha","year":"2022","journal-title":"Use Their Ratio Well. Inf. Sci."},{"key":"ref_37","first-page":"32","article-title":"A Revisiting Study of Appropriate Offline Evaluation for Top-N Recommendation Algorithms","volume":"41","author":"Zhao","year":"2022","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_38","first-page":"19","article-title":"The MovieLens Datasets: History and Context","volume":"5","author":"Harper","year":"2015","journal-title":"ACM Trans. Interact. Intell. Syst."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/5\/372\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:31:39Z","timestamp":1760031099000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/5\/372"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,13]]},"references-count":38,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["systems13050372"],"URL":"https:\/\/doi.org\/10.3390\/systems13050372","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,13]]}}}