{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:36:45Z","timestamp":1775266605981,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685694","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,20]]},"abstract":"<jats:p>Sequential recommendation aims to predict users\u2019 next preferred items according to their interaction sequences. Existing methods mainly utilize user-item interaction information, which may suffer from the issue of semantic information loss. In the paper, a Meta-Path guided Pre-training method for sequential Recommendation (MPPRec) is proposed to capture rich and meaningful semantic information between users and items. Specifically, MPPRec firstly learns the node embeddings guided by meta-paths in the pre-training phase. Then, the node embeddings are optimized according to task in the fine-tuning phase. Extensive experiments conducted on four real datasets demonstrate MPPRec outperforms the baseline methods.<\/jats:p>","DOI":"10.3233\/faia241414","type":"book-chapter","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:21Z","timestamp":1734947301000},"source":"Crossref","is-referenced-by-count":1,"title":["A Meta-Path Guided Pre-Training Method for Sequential Recommendation"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9641-4205","authenticated-orcid":false,"given":"Wenbing","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4054-3654","authenticated-orcid":false,"given":"Hongmei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming, China"}]},{"given":"Lihua","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming, China"}]},{"given":"Qing","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining X"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241414","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:21Z","timestamp":1734947301000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"ISBN":["9781643685694"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241414","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]}}}