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Current research focuses on the recommendation based on existing patents of a company; a few studies take into account the sequential pattern of patent acquisition activities and the possible diversity of a company\u2019s business interests. Moreover, the profiling of patents based on solely patent documents fails to capture the high-order information of patents. To bridge the gap, we propose a knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM) method for patent trading recommendation. KBiLSTM uses knowledge graph embeddings to profile patents with rich patent information. It introduces bidirectional long short-term memory network (BiLSTM) to capture the sequential pattern in a company\u2019s historical records. In addition, to address a company\u2019s diverse technology interests, we design an attention mechanism to aggregate the company\u2019s historical patents given a candidate patent. Experimental results on the United States Patent and Trademark Office (USPTO) data set show that KBiLSTM outperforms state-of-the-art baselines for patent trading recommendation in terms of F1 and normalised discounted cumulative gain (nDCG). The attention visualisation of randomly selected company intuitively demonstrates the recommendation effectiveness.<\/jats:p>","DOI":"10.1177\/01655515211023937","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T04:04:51Z","timestamp":1623643491000},"page":"814-830","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":16,"title":["Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)"],"prefix":"10.1177","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3272-3222","authenticated-orcid":false,"given":"Wei","family":"Du","sequence":"first","affiliation":[{"name":"School of Information, Renmin University of China, China"}]},{"given":"Guanran","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, China"}]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, China"}]},{"given":"Jian","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Information Systems, City University of Hong Kong, China"}]}],"member":"179","published-online":{"date-parts":[[2021,6,14]]},"reference":[{"key":"bibr1-01655515211023937","doi-asserted-by":"publisher","DOI":"10.1016\/j.wpi.2016.06.004"},{"key":"bibr2-01655515211023937","doi-asserted-by":"publisher","DOI":"10.1111\/iere.12338"},{"key":"bibr3-01655515211023937","doi-asserted-by":"crossref","unstructured":"Yanagisawa T, Guellec D. 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