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Traditional approaches typically rely on extensive historical interaction data or rich contextual information from paper abstracts, but often overlook critical temporal dependencies inherent in user preferences. A particular challenge is the recommendation of newly published papers, which, despite their significance in conveying cutting-edge research findings, suffer from sparse historical data. To address these issues, we propose the Meta-path Attention with Semantic Transformer for Academic Recommendation (MAPSTAR) framework, a novel recommendation model that integrates heterogeneous graph attention with transformer-based meta-path attention mechanisms. MAPSTAR simultaneously models both the temporal sequences of user interactions and the complex correlations among papers and their attributes. Specifically, our approach introduces a Transformer Encoder within the meta-path attention layer, allowing each meta-path embedding to capture global dependencies and dynamically adjust its representation based on contextual interactions with other meta-paths.<\/jats:p>","DOI":"10.1007\/s41060-026-01020-0","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T17:53:06Z","timestamp":1768845186000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Meta-path attention with semantic transformer for academic recommendation"],"prefix":"10.1007","volume":"22","author":[{"given":"Wei-Cheng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai-Yin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tung-Yang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming-Jiu","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun-Zhe","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiun-Long","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"1020_CR1","doi-asserted-by":"crossref","unstructured":"Pazzani, M.J., Billsus, D.: Content-based recommendation systems. 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