{"id":7412,"date":"2026-06-01T09:48:54","date_gmt":"2026-06-01T01:48:54","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=7412"},"modified":"2026-06-01T12:31:18","modified_gmt":"2026-06-01T04:31:18","slug":"jase-202609-32-063","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-063","title":{"rendered":"Accurate Matching and Recommendation for University Innovation Projects: A Multimodal Mobile Learning Approach"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Xiangge Liu, Bingquan Yin, Yali Hou, Benzhuo Fu, Qi Chen, and Haijuan Zhou<\/i><\/a><\/p>\n\n\n\n

Qinhuangdao Vocational and Technical College, Qinhuangdao 066000, China<\/p>\n<\/div>\n\n\n\n

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Received: March 1, 2026
Accepted: April 6, 2026
Publication Date: June 1, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

System Architecture for Collaborative Filtering-Based Project Recommendation <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.063<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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University innovation projects are increasingly complex and require personalized recommendation systems that align students\u2019 academic strengths with appropriate project opportunities. Existing recommendation methods typically rely on limited academic criteria, which fail to capture the full diversity of student learning characteristics, resulting in suboptimal matching. To address this, the research proposes an integrated mobile learning-based system that utilizes structured academic data from students\u2019 marksheets. The framework distinguishes multiple modalities, including overall academic performance (GPA, weighted GPA), subject domain proficiency (subject-wise scores), and temporal learning trends (semester-wise performance variations). Although these features originate from a single dataset, they represent distinct perspectives on student learning. Each modality is transformed into a feature representation, and these heterogeneous representations are combined into a unified multimodal student profile through vector-level fusion. A collaborative filtering based recommendation engine, powered by cosine similarity, then generates personalized project suggestions. The system\u2019s effectiveness is evaluated using standard recommender metrics such as Precision@5, Recall@5, F1@5, NDCG@5, MAP@5, hit rate, and coverage. Experimental results demonstrate the proposed method outperforms baseline approaches, achieving improvements of 12.6% in Precision@5 and 9.8% in NDCG@5, indicating enhanced ranking accuracy and recommendation relevance. Overall, the findings confirm that multimodal academic representations significantly improve personalized and large-scale innovation project recommendations in higher education.<\/p>\n\n\n\n

Keywords: Multimodal Recommendation, Mobile Learning, Collaborative Filtering, Innovation Project Matching, Educational Data Mining<\/em><\/p>\n\n\n\n

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  1. [1] O. O. Ayeni, N. M. Al-Hamad, O. N. N. Chisom, B. Osawaru, and O. E. Adewusi, (2024) \u201cAI in education: A review of personalized learning and educational technology\u201d GSC Advanced Research Review 18(2): 261\u2013271. DOI: 10.30574\/gscarr.2024.18.2.0062.<\/li>\n
  2. [2] R. A. Hurst, (2024) \u201cAnalysis of the impact of personalized learning paths in mobile learning platforms on the development of self-directed learning strategies among Brazilian college students\u201d Research Advances in Education 3(3): 9\u201314. DOI: 10.56397\/rae.2024.03.02.<\/li>\n
  3. [3] T. K\u00f6peczi-B\u00f3cz, (2024) \u201cThe impact of a combination of flipped classroom and project-based learning on the learning motivation of university students\u201d Education Sciences 14(3): 240. DOI: 10.3390\/educsci14030240.<\/li>\n
  4. [4] M. Z. I. Pranjol, P. Oprandi, and S. Watson, (2024) \u201cProject-based learning in biomedical sciences: Using the collaborative creation of revision resources to consolidate knowledge, promote cohort identity and develop transferable skills\u201d Journal of Biological Education 58(5): 1048\u20131064. DOI: 10.1080\/00219266.2022.2147576.<\/li>\n
  5. [5] H. Liao et al., (2025) \u201cAspect-enhanced explainable recommendation with multi-modal contrastive learning\u201d ACM Transactions on Intelligent Systems and Technology 16(1): 8:1\u20138:24. DOI: 10.1145\/3673234.<\/li>\n
  6. [6] A. Khelloufi et al., (2024) \u201cA multimodal latent-features-based service recommendation system for the social internet of things\u201d IEEE Transactions on Computational Social Systems 11(4): 5388\u20135403. DOI: 10.1109\/TCSS.2024.3360518.<\/li>\n
  7. [7] R. Alijani and F. Leonforte, (2025) \u201cPerformance analysis of multi-source heat pumps: A regression-based approach to energy performance estimation\u201d Sustainability 17(15): 6804. DOI: 10.3390\/su17156804.<\/li>\n
  8. [8] H. M. R. U. Rehman, M. Saleem, M. Z. Jhandir, E. S. Alvarado, H. Garay, and I. Ashraf, (2025) \u201cDetecting hate in diversity: A survey of multilingual code-mixed image and video analysis\u201d Journal of Big Data 12(1): 109. DOI: 10.1186\/s40537-025-01167-w.<\/li>\n
  9. [9] R. Nimri, M. J. H. Yang, C. L. Yang, and C. Arcodia, (2024) \u201cInvestigating student engagement of online experiential learning: A comparison of virtual and in-person winery field trips\u201d Journal of Teaching in Travel & Tourism 24(4): 405\u2013425. DOI: 10.1080\/15313220.2024.2402987.<\/li>\n
  10. [10] X. Ji, L. Sun, and K. Huang, (2025) \u201cThe construction and implementation direction of personalized learning model based on multimodal data fusion in the context of intelligent education\u201d Cognitive Systems Research 92: 101379. DOI: 10.1016\/j.cogsys.2025.101379.<\/li>\n
  11. [11] Y. Ye et al., (2025) \u201cHarnessing multimodal large language models for multimodal sequential recommendation\u201d Proceedings of the AAAI Conference on Artificial Intelligence 39(12): 13069\u201313077. DOI: 10.1609\/aaai.v39i12.33426.<\/li>\n
  12. [12] R. Whitehead, A. Nguyen, and S. J\u00e4rvel\u00e4, (2025) \u201cUtilizing multimodal large language models for video analysis of posture in studying collaborative learning: A case study\u201d Journal of Learning Analytics 12(1): 186\u2013200. DOI: 10.18608\/jla.2025.8595.<\/li>\n
  13. [13] M. Ogrizovi\u0107, D. Dra\u0161kovi\u0107, and D. Boji\u0107, (2024) \u201cQuality assurance strategies for machine learning applications in big data analytics: An overview\u201d Journal of Big Data 11(1): 156. DOI: 10.1186\/s40537-024-01028-y.<\/li>\n
  14. [14] X. Yang, (2025) \u201cResearch on personalized distance education recommendation system based on deep learning\u201d Scientific Reports 15(1): 42158. DOI: 10.1038\/s41598-025-26020-1.<\/li>\n
  15. [15] C. Cohn, C. Snyder, J. H. Fonteles, J. Montenegro, and G. Biswas, (2025) \u201cA multimodal approach to support teacher, researcher and AI collaboration in STEM+C learning environments\u201d British Journal of Educational Technology 56(2): 595\u2013620. DOI: 10.1111\/bjet.13518.<\/li>\n
  16. [16] D. Malitesta, G. Cornacchia, C. Pomo, F. A. Merra, T. Di Noia, and E. Di Sciascio, (2025) \u201cFormalizing multimedia recommendation through multimodal deep learning\u201d ACM Transactions on Recommender Systems 3(3): 1\u201333. DOI: 10.1145\/3662738.<\/li>\n
  17. [17] P. Liu, Y. Cao, and L. Wang, (2022) \u201cA multimodal fusion online music education system for universities\u201d Computational Intelligence and Neuroscience 2022: 652911. DOI: 10.1155\/2022\/652911.<\/li>\n
  18. [18] U. B. Qushem, A. Christopoulos, S. S. Oyelere, H. Ogata, and M.-J. Laakso, (2021) \u201cMultimodal technologies in precision education: Providing new opportunities or adding more challenges?\u201d Education Sciences 11(7): 338. DOI: 10.3390\/educsci11070338.<\/li>\n
  19. [19] Y. Zheng, (2025) \u201cResearch on recommendation algorithms in short video platforms: From traditional methods to deep learning and multimodal fusion\u201d Journal of Computer Signal Systems Research 2(5): 88\u2013100. DOI: 10.71222\/j5082f38.<\/li>\n
  20. [20] J. Chen, R. Yang, J. Guo, H. Wang, K. Wu, and L. Zhang, (2025) \u201cReliable service recommendation: A multi-modal adversarial method for personalized recommendation under uncertain missing modalities\u201d IEEE Transactions on Services Computing 18(3): 1724\u20131728. DOI: 10.1109\/TSC.2025.3556640.<\/li>\n
  21. [21] E. Wang and Z. Sun, (2025) \u201cResearch on personalized course resource recommendation method based on GEM-Rec\u201d Applied Sciences 15(3): 1075. DOI: 10.3390\/app15031075.<\/li>\n
  22. [22] C. Li, X. Weng, Y. Li, and T. Zhang, (2025) \u201cMulti-modal learning engagement assessment system: An innovative approach to optimizing learning engagement\u201d International Journal of Human\u2013Computer Interaction 41(5): 3474\u20133490. DOI: 10.1080\/10447318.2024.2338616.<\/li>\n
  23. [23] Y. Huang, W. Xu, P. Sukjairungwattana, and Z. Yu, (2024) \u201cLearners\u2019 continuance intention in multimodal language learning education: An innovative multiple linear regression model\u201d Heliyon 10(6): e28104. DOI: 10.1016\/j.heliyon.2024.e28104.<\/li>\n
  24. [24] X. Cui and M. Chen, (2024) \u201cA novel learning framework for vocal music education: An exploration of convolutional neural networks and pluralistic learning approaches\u201d Soft Computing 28(4): 3533. DOI: 10.1007\/s00500-023-09618-3.<\/li>\n
  25. [25] Y. Zhao, J. Guo, L. Wen, and L. Wang, (2025) \u201cMETRIC: Multiple preferences learning with refined item attributes for multimodal recommendation\u201d Journal of Information Intelligence 3(3): 242\u2013256. DOI: 10.1016\/j.jiixd.2025.04.001.<\/li>\n
  26. [26] Y. Chen, C. Huang, S. Gao, Y. Lyu, X. Chen, S. Liu, and C. Lv, (2025) \u201cA multimodal deep learning approach for legal English learning in intelligent educational systems\u201d Sensors 25(11): 3397. DOI: 10.3390\/s25113397.<\/li>\n
  27. [27] J. Duan, M. Zhang, M. Song, X. Xu, and H. Lu, (2025) \u201cEye Tracking-Enhanced Deep Learning for Medical Image Analysis: A Systematic Review on Data Efficiency, Interpretability, and Multimodal Integration\u201d Bioengineering 12(9): 954. DOI: 10.3390\/bioengineering12090954.<\/li>\n
  28. [28] S. Algarni and F. T. Sheldon, (2025) \u201cPersonalized Course Recommendations Leveraging Machine and Transfer Learning Toward Improved Student Outcomes\u201d Machine Learning and Knowledge Extraction 7(4): 138. DOI: 10.3390\/make7040138.<\/li>\n
  29. [29] F. Li, (2022) \u201cResearch on the management system of college students\u2019 innovation and entrepreneurship education based on b\/s architecture\u201d Journal of Applied Science and Engineering 26(5): 597\u2013604. DOI: 10.6180\/jase.202305_26(5).0002.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,720,6],"tags":[1472],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:\u00a0 BibTeX | http:\/\/dx.doi.org\/10.6180\/jase.202609_32.063\u00a0\u00a0 Download PDF University innovation projects are increasingly complex and require personalized recommendation systems…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/7412"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7412"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7412"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7412"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}