{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T10:56:50Z","timestamp":1760785010041,"version":"3.41.2"},"reference-count":39,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T00:00:00Z","timestamp":1629072000000},"content-version":"vor","delay-in-days":227,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019313","name":"Jiangsu Key Laboratory of Big Data Security and Intelligent Processing","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019313","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772285"],"award-info":[{"award-number":["61772285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Sequential recommendation system has received widespread attention due to its good performance in solving data overload. However, most of the sequential recommendation methods assume that user\u2019s preferences only depend on specific items in the current sequence and do not consider user\u2019s implicit interests. In addition, most of the previous works mainly focus on exploiting relationships between items in the sequence and seldom consider quantifying the degree of preferences for items implied by user\u2019s different behaviors. In order to address these above two problems, we propose an implicit preference\u2010aware sequential recommendation method based on knowledge graph (IPAKG). Firstly, this method introduces knowledge graph to exploit user\u2019s implicit preference representations. Secondly, we integrate recurrent neural network and attention mechanism to capture user\u2019s evolving interests and relationships between different items in the sequence. Thirdly, we introduce the concept of behavior intensity and design a behavior activation unit to exploit the degree of preferences for items implied by a user\u2019s different behaviors. Through the activation unit, the user\u2019s preferences on different items are further quantified. Finally, we conduct experiments on an Amazon electronics dataset and Tmall dataset to evaluate the performance of our method. Experimental results demonstrate that our proposed method has better performance than those baseline methods.<\/jats:p>","DOI":"10.1155\/2021\/5206228","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T20:09:28Z","timestamp":1629144568000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Implicit Preference\u2010Aware Sequential Recommendation Method Based on Knowledge Graph"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1053-587X","authenticated-orcid":false,"given":"Haiyan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7635-883X","authenticated-orcid":false,"given":"Kaiming","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7034-6459","authenticated-orcid":false,"given":"Jian","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5606-1951","authenticated-orcid":false,"given":"Yi","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","unstructured":"HidasiB. 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