{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T04:52:35Z","timestamp":1778734355860,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,14]]},"DOI":"10.1145\/3486622.3493949","type":"proceedings-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T01:18:53Z","timestamp":1649899133000},"page":"48-55","source":"Crossref","is-referenced-by-count":7,"title":["Cross-Domain Attentive Sequential Recommendations based on General and Current User Preferences (CD-ASR)"],"prefix":"10.1145","author":[{"given":"Nawaf","family":"Alharbi","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Science and Arts in Uglat Asugour, Qassim University, Buraydah, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Doina","family":"Caragea","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Kansas State University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Nawaf Alharbi and Doina Caragea. 2021. Cross-Domain Self-Attentive Sequential Recommendations. In ICDSA. Nawaf Alharbi and Doina Caragea. 2021. Cross-Domain Self-Attentive Sequential Recommendations. In ICDSA.","DOI":"10.1007\/978-981-16-5348-3_48"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Tim Donkers Benedikt Loepp and J\u00fcrgen Ziegler. 2017. Sequential user-based recurrent neural network recommendations. In RecSys. 152\u2013160. Tim Donkers Benedikt Loepp and J\u00fcrgen Ziegler. 2017. Sequential user-based recurrent neural network recommendations. In RecSys. 152\u2013160.","DOI":"10.1145\/3109859.3109877"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2896881"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959152"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109882"},{"key":"e_1_3_2_1_6_1","volume-title":"Fusing similarity models with markov chains for sparse sequential recommendation","author":"He Ruining","unstructured":"Ruining He and Julian McAuley . 2016. Fusing similarity models with markov chains for sparse sequential recommendation . In ICDM. IEEE , 191\u2013200. Ruining He and Julian McAuley. 2016. Fusing similarity models with markov chains for sparse sequential recommendation. In ICDM. IEEE, 191\u2013200."},{"key":"e_1_3_2_1_7_1","unstructured":"Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW. 507\u2013517. Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW. 507\u2013517."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2831682"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173\u2013182. Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173\u2013182.","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Yun He Yin Zhang Weiwen Liu and James Caverlee. 2020. Consistency-Aware Recommendation for User-Generated Item List Continuation. In WSDM. 250\u2013258. Yun He Yin Zhang Weiwen Liu and James Caverlee. 2020. Consistency-Aware Recommendation for User-Generated Item List Continuation. In WSDM. 250\u2013258.","DOI":"10.1145\/3336191.3371776"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Bal\u00e1zs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In CIKM. 843\u2013852. Bal\u00e1zs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In CIKM. 843\u2013852.","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_2_1_12_1","unstructured":"Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939(2015). Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939(2015)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271684"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.07.091"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Santosh Kabbur Xia Ning and George Karypis. 2013. Fism: factored item similarity models for top-n recommender systems. In KDD. 659\u2013667. Santosh Kabbur Xia Ning and George Karypis. 2013. Fism: factored item similarity models for top-n recommender systems. In KDD. 659\u2013667.","DOI":"10.1145\/2487575.2487589"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM. 197\u2013206. Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM. 197\u2013206.","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_1_18_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).","author":"Kingma P","year":"2014","unstructured":"Diederik\u00a0 P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014). Diederik\u00a0P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD. 426\u2013434. Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD. 426\u2013434.","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_3_2_1_20_1","volume-title":"Int. Conf. on machine learning. PMLR, 82\u201390","author":"Lee Joonseok","year":"2013","unstructured":"Joonseok Lee , Seungyeon Kim , Guy Lebanon , and Yoram Singer . 2013 . Local low-rank matrix approximation . In Int. Conf. on machine learning. PMLR, 82\u201390 . Joonseok Lee, Seungyeon Kim, Guy Lebanon, and Yoram Singer. 2013. Local low-rank matrix approximation. In Int. Conf. on machine learning. PMLR, 82\u201390."},{"key":"e_1_3_2_1_21_1","unstructured":"Bin Li Qiang Yang and Xiangyang Xue. 2009. Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In IJCAI. Citeseer. Bin Li Qiang Yang and Xiangyang Xue. 2009. Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In IJCAI. Citeseer."},{"key":"e_1_3_2_1_22_1","unstructured":"Jing Li Pengjie Ren Zhumin Chen Zhaochun Ren Tao Lian and Jun Ma. 2017. Neural attentive session-based recommendation. In CIKM. 1419\u20131428. Jing Li Pengjie Ren Zhumin Chen Zhaochun Ren Tao Lian and Jun Ma. 2017. Neural attentive session-based recommendation. In CIKM. 1419\u20131428."},{"key":"e_1_3_2_1_23_1","unstructured":"Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In KDD. 305\u2013314. Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In KDD. 305\u2013314."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Jianxun Lian Fuzheng Zhang Xing Xie and Guangzhong Sun. 2017. CCCFNet: a content-boosted collaborative filtering neural network for cross domain recommender systems. In WWW. 817\u2013818. Jianxun Lian Fuzheng Zhang Xing Xie and Guangzhong Sun. 2017. CCCFNet: a content-boosted collaborative filtering neural network for cross domain recommender systems. In WWW. 817\u2013818.","DOI":"10.1145\/3041021.3054207"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412247"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Minh-Thang Luong Hieu Pham and Christopher\u00a0D Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025(2015). Minh-Thang Luong Hieu Pham and Christopher\u00a0D Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025(2015).","DOI":"10.18653\/v1\/D15-1166"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331200"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Tong Man Huawei Shen Xiaolong Jin and Xueqi Cheng. 2017. Cross-Domain Recommendation: An Embedding and Mapping Approach.. In IJCAI. 2464\u20132470. Tong Man Huawei Shen Xiaolong Jin and Xueqi Cheng. 2017. Cross-Domain Recommendation: An Embedding and Mapping Approach.. In IJCAI. 2464\u20132470.","DOI":"10.24963\/ijcai.2017\/343"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.433"},{"key":"e_1_3_2_1_30_1","unstructured":"Weike Pan Nathan\u00a0N Liu Evan\u00a0W Xiang and Qiang Yang. 2011. Transfer learning to predict missing ratings via heterogeneous user feedbacks. In IJCAI. 2318. Weike Pan Nathan\u00a0N Liu Evan\u00a0W Xiang and Qiang Yang. 2011. Transfer learning to predict missing ratings via heterogeneous user feedbacks. In IJCAI. 2318."},{"key":"e_1_3_2_1_31_1","volume-title":"BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618(2012).","author":"Rendle Steffen","year":"2012","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2012 . BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618(2012). Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618(2012)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Steffen Rendle Christoph Freudenthaler and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW. 811\u2013820. Steffen Rendle Christoph Freudenthaler and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW. 811\u2013820.","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_2_1_33_1","volume-title":"Matrix factorization in cross-domain recommendations framework by shared users latent factors. Procedia computer science 143","author":"Sahu K","year":"2018","unstructured":"Ashish\u00a0 K Sahu and Pragya Dwivedi . 2018. Matrix factorization in cross-domain recommendations framework by shared users latent factors. Procedia computer science 143 ( 2018 ), 387\u2013394. Ashish\u00a0K Sahu and Pragya Dwivedi. 2018. Matrix factorization in cross-domain recommendations framework by shared users latent factors. Procedia computer science 143 (2018), 387\u2013394."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-01402-3"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.02.024"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Ajit\u00a0P Singh and Geoffrey\u00a0J Gordon. 2008. Relational learning via collective matrix factorization. In KDD. 650\u2013658. Ajit\u00a0P Singh and Geoffrey\u00a0J Gordon. 2008. Relational learning via collective matrix factorization. In KDD. 650\u2013658.","DOI":"10.21236\/ADA486804"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Yong\u00a0Kiam Tan Xinxing Xu and Yong Liu. 2016. Improved recurrent neural networks for session-based recommendations. In DLRS. 17\u201322. Yong\u00a0Kiam Tan Xinxing Xu and Yong Liu. 2016. Improved recurrent neural networks for session-based recommendations. In DLRS. 17\u201322.","DOI":"10.1145\/2988450.2988452"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM. 565\u2013573. Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM. 565\u2013573.","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_2_1_39_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. arXiv preprint arXiv:1706.03762(2017). Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. arXiv preprint arXiv:1706.03762(2017)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/1148170.1148257"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-017-0032-6"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.08.008"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Haochao Ying Fuzhen Zhuang Fuzheng Zhang Yanchi Liu Guandong Xu Xing Xie Hui Xiong and Jian Wu. 2018. Sequential recommender system based on hierarchical attention network. In IJCAI. Haochao Ying Fuzhen Zhuang Fuzheng Zhang Yanchi Liu Guandong Xu Xing Xie Hui Xiong and Jian Wu. 2018. Sequential recommender system based on hierarchical attention network. In IJCAI.","DOI":"10.24963\/ijcai.2018\/546"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Fajie Yuan Alexandros Karatzoglou Ioannis Arapakis Joemon\u00a0M Jose and Xiangnan He. 2019. A simple convolutional generative network for next item recommendation. In WSDM. 582\u2013590. Fajie Yuan Alexandros Karatzoglou Ioannis Arapakis Joemon\u00a0M Jose and Xiangnan He. 2019. A simple convolutional generative network for next item recommendation. In WSDM. 582\u2013590.","DOI":"10.1145\/3289600.3290975"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3029002"},{"key":"e_1_3_2_1_46_1","unstructured":"Andrew Zimdars David\u00a0Maxwell Chickering and Christopher Meek. 2013. Using temporal data for making recommendations. arXiv preprint arXiv:1301.2320(2013). Andrew Zimdars David\u00a0Maxwell Chickering and Christopher Meek. 2013. Using temporal data for making recommendations. arXiv preprint arXiv:1301.2320(2013)."}],"event":{"name":"WI-IAT '21: IEEE\/WIC\/ACM International Conference on Web Intelligence","location":"ESSENDON VIC Australia","acronym":"WI-IAT '21","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence"]},"container-title":["IEEE\/WIC\/ACM International Conference on Web Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3486622.3493949","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3486622.3493949","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:06Z","timestamp":1750191126000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3486622.3493949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,14]]},"references-count":46,"alternative-id":["10.1145\/3486622.3493949","10.1145\/3486622"],"URL":"https:\/\/doi.org\/10.1145\/3486622.3493949","relation":{},"subject":[],"published":{"date-parts":[[2021,12,14]]}}}